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    "#  17\\.  支持向量机实现与应用  # \n",
    "\n",
    "##  17.1.  介绍  # \n",
    "\n",
    "在前面的实验中，我们对线性分布和非线性分布的数据处理方法进行了简单的介绍和实验操作。当前还有一种机器学习方法，它在解决小样本、非线性及高维模式识别中都表现出了许多独特的优势。同时，其不仅可以应用于线性分布数据，还可以用于非线性分布数据。相比于其他基本机器学习分类算法如逻辑回归、KNN、朴素贝叶斯等，其最终效果的表现一般都会优于这些方法。 \n",
    "\n",
    "##  17.2.  知识点  # \n",
    "\n",
    "  * 线性分类支持向量机 \n",
    "\n",
    "  * 拉格朗日对偶性 \n",
    "\n",
    "  * 线性支持向量机分类实现 \n",
    "\n",
    "  * 非线性分类支持向量机 \n",
    "\n",
    "  * 核技巧与核函数 \n",
    "\n",
    "##  17.3.  线性分类支持向量机  # \n",
    "\n",
    "逻辑回归的实验中，我们尝试通过一条直线针对线性可分数据完成分类。同时，实验通过最小化对数损失函数来找到最优分割边界，也就是下图中的紫色直线。 \n",
    "\n",
    "![image](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017115.png)\n",
    "\n",
    "逻辑回归是一种简单高效的线性分类方法。而在本次实验中，我们将接触到另一种针对线性可分数据进行分类的思路，并把这种方法称之为支持向量机（英语：Support Vector Machine，简称：SVM）。 \n",
    "\n",
    "如果你第一次接触支持向量机这个名字，可能会感觉读起来比较拗口。至少我当年初次接触支持向量机时，完全不知道为什么会有这样一个怪异的名字。假如你和当年的我一样，那么当你看完下面这段介绍内容后，就应该会对支持向量机这个名词有更深刻的认识了。 \n",
    "\n",
    "##  17.4.  支持向量机分类特点  # \n",
    "\n",
    "假设给定一个训练数据集  $T=\\lbrace(x_1,y_1),(x_2,y_2),\\cdots ,(x_n,y_n)\\rbrace$  。同时，假定已经找到样本空间中的分割平面，其划分公式可以通过以下线性方程来描述： \n",
    "\n",
    "$$ wx+b=0 $$ \n",
    "\n",
    "使用一条直线对线性可分数据集进行分类的过程中，我们已经知道这样的直线可能有很多条： \n",
    "\n",
    "![image](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017365.png)\n",
    "\n",
    "问题来了，哪一条直线是最优的划分方法呢？ \n",
    "\n",
    "在逻辑回归中，我们引入了 S 形曲线和对数损失函数进行优化求解。如今，支持向量机给了一种从几何学上更加直观的方法进行求解，如下图所示： \n",
    "\n",
    "[ ![https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017647.png](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017647.png) ](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017647.png)\n",
    "\n",
    "上图展示了支持向量机分类的过程。图中  $wx+b=0$  为分割直线，我们通过这条直线将数据点分开。与此同时，分割时会在直线的两边再设立两个互相平行的虚线，这两条虚线与分割直线的距离一致。这里的距离往往也被我们称之为「间隔」，而支持向量机的分割特点在于，要使得分割直线和虚线之间的间隔最大化。同时也就是两虚线之间的间隔最大化。 \n",
    "\n",
    "对于线性可分的正负样本点而言，位于  $wx+b=1$  虚线外的点就是正样本点，而位于  $wx+b=-1$  虚线外的点就是负样本点。另外，正好位于两条虚线上方的样本点就被我们称为支持向量，这也就是支持向量机的名字来源。 \n",
    "\n",
    "##  17.5.  支持向量机分类演示  # \n",
    "\n",
    "下面，我们使用 Python 代码来演示支持向量机的分类过程。 \n",
    "\n",
    "首先，我们介绍一种新的示例数据生成方法。即通过 scikit-learn 提供的 ` make_blobs  ` 方法生成团状数据。 "
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x, y = make_blobs(n_samples=60, centers=2, random_state=30, cluster_std=0.8)  # 生成示例数据\n",
    "\n",
    "plt.figure(figsize=(12, 8))  # 绘图\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb30590b",
   "metadata": {},
   "source": [
    "**-----以下是代码解释-----**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b19ed9",
   "metadata": {},
   "source": [
    "# 代码分析\n",
    "\n",
    "这段代码展示了如何使用 scikit-learn 生成二维空间中的聚类数据并进行可视化。让我分步骤解释：\n",
    "\n",
    "## 1. 导入必要的库\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c2427f",
   "metadata": {},
   "source": [
    "```\n",
    "from sklearn.datasets import make_blobs  # 用于生成聚类数据\n",
    "import matplotlib.pyplot as plt  # 用于数据可视化\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b26d06",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 2. 设置 Jupyter Notebook 的绘图模式\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c472568",
   "metadata": {},
   "source": [
    "```\n",
    "%matplotlib inline  # 使图表直接显示在notebook中\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8c0864",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 3. 生成示例数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a3ffb7",
   "metadata": {},
   "source": [
    "```\n",
    "x, y = make_blobs(\n",
    "    n_samples=60,      # 生成60个样本点\n",
    "    centers=2,         # 指定2个中心点(两类数据)\n",
    "    random_state=30,   # 随机种子，确保结果可重现\n",
    "    cluster_std=0.8    # 聚类的标准差，控制数据的分散程度\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544874c6",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 4. 绘制散点图\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198ae356",
   "metadata": {},
   "source": [
    "```\n",
    "plt.figure(figsize=(12, 8))  # 设置图形大小为12x8英寸\n",
    "plt.scatter(\n",
    "    x[:, 0],          # x轴数据(第一个特征)\n",
    "    x[:, 1],          # y轴数据(第二个特征)\n",
    "    c=y,              # 点的颜色由类别标签决定\n",
    "    s=40,             # 点的大小为40\n",
    "    cmap=\"bwr\"        # 使用蓝-白-红的颜色映射\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153ad5a6",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 代码功能\n",
    "这段代码用于生成支持向量机(SVM)分类问题的示例数据：\n",
    "1. 生成两类数据点，每类30个点\n",
    "2. 数据点在二维平面上呈现出两个集群\n",
    "3. 不同类别的点用不同颜色表示\n",
    "4. 数据的分布具有一定的随机性，但保持可分性\n",
    "\n",
    "生成的数据将用于后续SVM分类器的训练和测试。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526ee4b3",
   "metadata": {},
   "source": [
    "**------以上是代码解释------**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc5c250",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "接下来，我们在示例数据中绘制任意 3 条分割线把示例数据分开。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c969e926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmTK6+eabtWfPHkWTsAU569ev18iRI/XII48k+pgBAwa46Mx/KVasWLiWBwBphr202nwcfzqaBTsW0NjHc86RRo+WbrzR61UCALyyZMkSVa9eXa1bt3bBTZEiRbRjxw7FdLpajx499Pzzzwd9zKpVq1S2bNnA9T///FN169ZVvXr19MorrwTdybGLn+3kWKBDuhoApL5Dh6T335emTfO1jb70UunBB6UiRbxeGQDAC/v27dNTTz2lUaNGuZ2cnDlzql+/fmrbtq0yZMgQVelqKQ5yLIrbtWtX0MeUKFFCmTJlcp9v3rzZBTfVqlXTG2+8ofSJ5UckgJocAAAAILT++ecfffTRR+rcubO2/Ds/4LbbbtPQoUPdLk6kSElskOKQLH/+/O6SHLaDU79+fV1xxRV6/fXXUxTgAAAAAAitdevWqX379ppm2/qS64o8ZswYNWrUSNEsZPtOFuDYDs6FF17oWkbHzeMrVKhQqL4tAAAAgCQcOXJEAwcOdBcrF8mcObN69uypxx9/XFmyZFG0C1mQM336dNdswC5FixaNd18Yu1YDAELIzl+NGye9+qrv83z5pPvvl9q0kQoU8Hp1AICE2K5Nu3bt3HG6ady4sUaPHu12cWJFWOfkpBQ1OQAQudatk+rUsRkKvrbTftahzYKdOXOkMmW8XCEA4NRMqy5durj6G1O4cGENHz5cLVu2VLoomBsQsXNyAACxwYKa66/37d7EDXCMDRnduVO67rrT7wMAhN/ff/+tF198UeXKlXMBjtXJd+rUSatXr9att94aFQFOSnnfCw4AEHVmzZJWr078fgt0bKfH6libNAnnygAAcc2fP19t2rTR0qVL3fWrrrpK48aN06U2NyCGsZMDAEgxGyaaMWPwx9j99jgAQPj99ddfeuSRR1SjRg0X4OTJk8cFN/PmzYv5AMewkwMASDEbHpqajwMApA4rt3/rrbf02GOPBbob33vvvRo0aJAKpKGOMAQ5AIAUu+yypAMYu98eBwAIjxUrVqht27aaO3euu16+fHmNHTtWdaxLTBpDuhoAIMVuuUXKnVtKrFbVbrfGN7fdFu6VAUDac/DgQfXo0cOloVmAky1bNjf/ZsmSJWkywDHs5AAAUixrVuntt6UWLf5rNOCXPr0vyHnzTSlbNs+WCABpwhdffKEOHTpow4YN7vr111+vESNG6MILL1Raxk4OAOCMNG8uff21VKtW/Nvt+syZ/wVAAIDU98cff+iGG25wFwtwLrjgAn3++efucmEaD3AMOzkAgDNWu7Y0e7a0aZO0bZtkNa3Finm9KgCIXceOHdOwYcPUt29fHT58WBkyZFC3bt305JNPKnv27F4vL2IQ5AAAzlrRor4LEA6WHjlpkvTll9Lhw1ZcLd13n01v93plQGhZvY3NvFm5cqW7XrduXY0ZM8Y1GEB8BDkAACBq/PKLb8Ds+vVShgzWLtd3efppadAgqUsXr1cIpL7t27e7ltDWGtrkz59fQ4YM0d133610iXWASeOoyQEAAFFh3z6pXj3pt9981//+27erc/Kk72PXrr6GF0CsOHnypMaPH6+yZcu6AMcCGhvwuXr1at1zzz0EOEEQ5AAAgKhgJ7H//DN+N79TPfWUL+gBot3SpUtVo0YNtW7dWrt373btob///nuNGzdO5513ntfLi3gEOQAAICr8m6kT1MaN0oIF4VgNEBr79u1Tp06ddMUVV2jBggXKmTOnXnzxRS1atEhXXXWV18uLGtTkAACAqLBjh6/+Jik7d4ZjNUDq+ueff/Txxx+rc+fO2rx5s7vttttu09ChQ1WkSBGvlxd12MkBAABRwUZ/2LDZpNDpD9Fm/fr1atq0qQtqLMApWbKkpk6dqg8++IAA5wwR5ACISlu3SvPnS8uXJ+/MLoDo98ADwettrAa7QgXp0kvDuSrgzB05csTNu6lYsaILajJnzqw+ffro559/VuPGjb1eXlQjyAEQVWw0wHXXSXZiq3p16ZJLpFKlpDfe8HplAELt1lulyy6Tzjnn9Pv8TaYGD/7vcyCSTZ8+XZdccokLao4ePeqCGgtuevfurSxZsni9vKhHkAMgaixbJlnN5eTJ8Xdvfv3VNwiwTx8vVwcg1DJntgNDqUED33ULdjJm9H2eJ4/0ySdS06aeLhFIkqWj3X777S6osTS1woUL68MPP9SUKVNUys7aIVWk+8eqnCK4u0SuXLm0d+9enXvuuV4vB4DHqlSxlprB28da+pqlqwCI/V3diROlw4elcuWkFi2kTJm8XhWQuL///ltjxozRk08+qf379yt9+vTq2LGjS1fjODf1YwO6qwGICkuWSD/+GPwxNv183Dhp5MhwrQqAV8qX912AaLBw4UI372aJvZnJshKu0tixY3WZ5V8iJEhXAxA1qWpJsenn/75/AADgORviacFNtWrVXICTO3dujR8/XvPmzSPACTF2cgBEheSmoVjOPgAAXrJqkLffflvdunXTDhvwJOnee+/VoEGDVKBAAa+XlyYQ5ACICvXr+4qMg9Xj2PwMio4BAF5auXKl2rZtqzlz5rjr5cuXd6lpderU8XppaQrpagCiQuHC0h13JNw61h/gZM3q67IGAEC4HTp0SD179lTlypVdgJM1a1YNHDjQpakR4IQfOzkAosbo0dK6ddKCBb6gxj8U0AIfS2f78kspb16vVwkASGu+/PJLdejQQX/88Ye7fv3112vEiBG68MILvV5amsVODoCoYd0ibff/1Velyy+XcueWihaVOnf2tZO1lDYAAMJlw4YNatGihQtqLMC54IILNGHCBH3++ecEOB5jTg4AAACQAsePH9ewYcPcjBtLU8uQIYO6du2qp556StmzZ/d6eTGLOTkAAABACMydO1dt2rRxDQZM7dq1XWOBCkyijiikqwEAAABJsFbQrVq1Ut26dV2Aky9fPr3xxhuuyQABTuQhyAEAAAAScfLkSb300ksqU6aM3nzzTXfbww8/rDVr1rjZN+nSpfN6iUgA6WoAAABAApYuXepS0+bPn++uW3vocePGqVq1al4vDUlgJwcAAACIY//+/ercubOuuOIKF+DkyJHDNRr44YcfCHCiBDs5AAAAgCRrOvzJJ5+oU6dO2rx5s7utZcuWLsA5//zzvV4eUoAgBwAAAGneL7/8onbt2mnq1Knu+sUXX6zRo0frmmuu8XppOAOkqwEAAKSATRg8ftzrVSC1HD16VM8884zrkGYBTqZMmdS7d28tX76cACeKEeQAAAAkw08/SffcI2XLJmXKJBUqJPXuLf31l9crw5maMWOGLrnkEhfUWLDTqFEjF9z06dNHWbJk8Xp5OAsEOQAAAEmYNEmqUkV6/33pyBHfbdu2Sc89J11xhfRv+QaixJYtW3THHXe4oGbdunUqXLiwPvjgA7eTU6pUKa+Xh1RAkAMAABDEzp1WfC79/bfvEteJE9KmTb4dHkS+EydOaOTIkSpbtqwLatKnT6+OHTtq9erVuu2225h5E0NoPAAAABDE669b3YavFichFvjMnCmtXi2VLRvu1SG5Fi5cqNatW2vJkiXuetWqVTV27FhdfvnlXi8NIcBODgAAQBCzZ9vU+6QfN3duOFaDlNq9e7cb6GnzbSzAyZ07twtu5s2bR4ATw9jJAQAACMJS0pJiWU7JCYQQ3pk377zzjrp166bt27e72+6++24NHjxYBQsW9Hp5CDGCHABAyP3xh/TRR9KuXVLRotLtt0v58nm9KiB5bMD99OnBgxhLZbvqqnCuCsGsWrVKbdu21WzbhpNUrlw5jRkzRvXq1fN6aQgT0tUAACFjdQwPPCAVLy717CkNHSo9+qhUpIjUp0/iNQ5AJHnoId9OTWLOOcfXee2yy8K5KiTk0KFD6tWrlypXruwCnKxZs6p///5aunQpAU4aQ5ADAAiZ++6T3njDF8xYyo8NULSz4faxb1/p2We9XiGQtPPPl15+2RfoWEATV4YMUq5c0ttve7U6+E2cOFHly5fXgAEDdPz4cTVv3lwrV65Uz5493YBPpC0EOQCAkA1OtJkiwVJ8+ve3ouBwrgo484B92jSpdu3/bsuc2dc6+scf6armpQ0bNujGG2/Uddddpz/++EPFihXTZ599pi+++EIXXXSR18uDR6jJAQCExFtv+c5ynzpXJK5jx6SPP5YefjicK0vb9u6VPvhA+vVX6dxzpZtv5gA9uRo29F3++kvat08qUEDKls3rVaVdtlszbNgw9e3b16WpZciQQZ07d9bTTz+tHDlyeL08eIwgBwAQEjYBPqluUxYEMSk+fEaOlLp399VK2c/efj9PPindcIMv3SpnTq9XGB3OO893gXe++eYb1xZ6xYoV7nrt2rVdY4GKFSt6vTRECNLVAAAhkT+/lD6Jdxnb5bHHIfTGj5c6dpSOHPHVSFldlL818sSJ0vXX0wIZkW/Hjh267777VKdOHRfg5MuXT6+//rrmzJlDgIN4CHIAACFx553BU9WMFXG3bBmuFaVdtnPTq1fi91uwY512Z84M56qA5Dt58qRefvlllS1bVm9YNxPX9e4hrV69Wq1atVK6YO3vkCYR5AAAQuLKK6WmTU/vRuVnxyQdOvjqGhBaU6f66kiCsd/Tv8eOQERZtmyZatWqpYcfflh//fWXaw89b948vfTSS8qbN6/Xy0OEIsgBAISEBTE2ANQCHWM1IHaxg2m7r3VradAgr1eZNmzZkvRjbDdn06ZwrAZInv3796tLly664oor9P3337tmAkOHDtUPP/yg6tWre708RDgaDwAAQsYaHH35pbR0qfTee9KuXVLRor62uxdf7PXq0o7k1D1Z8FmoUDhWAwT3zz//6NNPP1WnTp30559/uttatmzpOqmdb0OLgGQgyAEAhNyll/ou8EaTJr520db2ONhOzl13KewOH5Z++03KmNEX+CbVrAKx7ZdfflH79u01ZcoUd/3iiy/WqFGj1MSexEAK8FICAECMs1kuTz0VfBenShXp2mvDt6Y9e6TOnX01WRUqSKVLSza3cfhwurylRUePHtWzzz7rOqRZgJMpUyY37+bnn38mwMEZYScHAIA0oGtX6cAB6dlnfTVR/mZU1gHPyhs++yzxJhGhCHBq1pTWrPmvjbXZuFHq0kX68UfpzTfZ1UkrZs6cqbZt22rt2rXuesOGDTV69GiVtsgXOEPp/rHExwi1b98+5cqVS3v37tW5ts8OAADOig1ftS5qliJmwz+thXe1av8FPeFg83rGjIkf4Jzq44+lW24J35oQflu2bFHXrl31/vvvu+uFChVydTe33XYbLaFx1rEBQQ4AAAibgwd9KWqHDiX+GNtRsp2eOXPCuTKEy4kTJzRmzBg9+eST7lgvffr0ateunUtXs+M+IDViA9LVAABA2KxfHzzAMbbDYylriD2LFi1S69attXjxYnf9yiuv1Lhx43T55Zd7vTTEGLJdAQBA2NispOQIV30QwmPPnj2u7uaqq65yAY6djbfdHJt/Q4CDUCDIAYBUsHWr1K+fnZWULrnE14r3u+9s3oPXKwMiS5kyUsGCSQdCjRuHa0UIJauKeOedd1SmTBmNHTvWXb/rrru0Zs0atWnTRucQzSIag5zrr79eF1xwgbJkyaLChQvr7rvv1mareASAGDJrlm++R+/e0g8/SMuXSx9+KNWqJbVtSztc4NQA5tFHgzc6sI5v9hhEt1WrVunqq692x3/bt29X2bJlNWvWLL399tsqmFSkC0RykFO/fn199NFHLlq3ybU24OkWWqUAiCEbNkjNm0tHjsQPZuwgzYwbJw0d6tnygIj02GNSixa+z+O2ifansg0Z4jtJgOh06NAhPfHEE6pcubJmz57tTnb3799fy5Ytc8eGQDiEtbvaF198oRYtWriBTxlttHES6K4GINL16iUNGhS8Fa51ktq0yTfRHYCP/c1Y5+ARI6Rly3wBTqNGvgGhdet6vTqcqYkTJ6pDhw76/fff3fVmzZpp5MiRKl68uNdLQwyIyO5qf/31l959913VqFEj0QDHgh+7xP2PAEAk++ST4AGO2b7d1wrXOkodPiyVL++r2wHSMivFsNo1uyD6bdiwQY8++qgmTJjgrhctWlQjRoxwJ7eZeYOYbDzw+OOPK3v27MqbN6/7A/j8888TfeyAAQNcdOa/FCtWLNTLA4CznvmRHNdeK91wg3T77VKlSlLVqtK/HVQBIGodP35cgwcPVrly5VyAkyFDBj322GOuHufGG28kwEH0BDk9evRwT9hgl9WrVwceb0/0JUuWaNq0aa6Dxj333OM6aySkZ8+ebvvJf9m4cePZ/e8AIMTKlUteq9vjx+NftwDHag6WLAnZ0gAgpL799lvX/rl79+6uDqdWrVrumG/QoEHKkSOH18tDGpfimpwdO3Zo165dQR9TokQJZcqU6bTbN23a5HZn5s2bp+rVqyf5vajJARDpPv1UOtN+KhYc1aghzZ2b2qsCgNCxY0HL1Hn99dfddcvWsd2ce++9V+njdpIAoqkmJ3/+/O5yJk7+23oobt0NAEQz6xDVtKk0derpraItSyPYaSSr5fnmG2ntWql06ZAvFQDOih3Hvfrqqy6rx2qtzYMPPqiBAwe6QAeIJCELtxcsWKBRo0Zp6dKl+uOPP1xf9DvuuEMXX3xxsnZxACAa2G7MZ59JHTtKWbLEvy+BDe0ErVsXkqUBQKqx9s+Wjvbwww+7AKdSpUr67rvv9PLLLxPgIG0FOdmyZdP//d//qUGDBm7K7QMPPOD+IObMmaPMmTOH6tsCQNjZS9qwYdK2bdKXX0offyytXCmVKpW8f589e6hXCABnZv/+/erSpYuuuOIKff/9967WZujQofrxxx9dx1wgUoWshfQll1zidm8AIK2w9GAbDOrXsqUv2Dk1jS2u886T2NwGEGmsZNsGuXfq1El//vmnu80Gug8bNsy1hwYiHdVhABAiDz/s26UJVofbrZtvJwihc+SINGWK9NFH0o8/Bq+TAiD98ssvuvbaa9WyZUsX4FhDqa+++koff/wxAQ6iBkEOAIRIoULS5MmnBzo22d08+KDNEvNseTHPdtCee873e7DmELfdJlWp4ptTNHu216sDIo81hnr22WdVsWJFTZkyxXXKfeqpp7R8+XI1tT8iIJZbSIcTLaQBxIKdOyXrtGoNCg4dsnReqXVrX/to5uSFTtu20tixp99uAaddbHenQQMvVgZEnpkzZ6pt27Zaa+0eZX8bDTR69GhXVw1EY2xAkAMAiDmWlma7NomxIKdECV/77kgNNPfvlxYu9A2SrVxZKlzY6xUhFm3ZskVdu3bV+++/764XKlTINRa4/fbb3YB3IFpjA9LVACCG0rNmzJDuv1+6/nqpTRtr5582a1Befvm/tMDEflbr1/vmFEWaw4elTp2kggWlhg19qXZWBmFDZ/+t/wbO2okTJ9yoj7Jly7oAx4Z4tm/fXqtXr3YjPwhwEO1C1l0NABA+NpfvuuukefN8B/d//+37OG6cdOON0nvvnT7HJ5atWuX7GSRlzRqpTh1FjGPHfEGNBV9xu/LZ559/Ls2fLy1axK4Ozs6iRYvUpk0b1wbaVKlSRePGjXNtooFYwU4OAEQ526lp0cK3a2P8B/f+j3Zw/MgjSlNy5EheGlqkzSh6801pzpyE247b79NmMT31lBcrQyzYs2eP2rVrp6uuusoFOJb2Y3U38+fPJ8BBzCHIAYAoZ7s3dub/xImE77cD5rffljZsUFQFbvb/sjbctrNx113SpEmJ/x9PZUFfUml6GTNK11yjiDJ6dPCW4xbovPuur14HSC4rv37nnXdcE4ExY8a463fddZdLTbNmA+ecc47XSwRSHUEOAES5Dz8MXn9ibFfjk08UNXNtbrpJqlnT15XOuqB98IFv0GrVqtL27Ul/jf/9z1fTktixmwUS1sI7b15FXJpdsOGx/p/PH3+Ea0WIdhbINGzYUHfffbe2b9/uAh3rpPb222+7JgNArCLIAYAot3t30rsWdrBvj4sG7dpJX3wRP+XOv4Pz00++YCep/6+loU2fLp13ni/A86eu+YNB2x0aNkwRJ7l1U2mpvgpn5tChQ3riiSdUqVIlzZo1S1myZFG/fv20bNkyXX311V4vDwg5ghwAiHIXXpj0YyxYSM7jvLZpk/TGG4nvZtj/wwrvkzPM0+YRrVsnvfiibyZRxYq+5gw2oNWCqMyZFXGsK16wXTkL1kqWlC6+OJyrQrSZNGmSKlSooP79++v48eO69tprtXLlShf0ZI7EJz4QAgQ5ABDlWrVKulbFzvzfeqsing1MTYoFAZailxy5ckkdOkjffiv9/LP0f/8nNWkSvO7FS9Y6Oli6mu1gPf545M72gbc2btyom266Sc2bN9fvv/+uokWL6v/+7/80ceJEFS9e3OvlAWEVoS/zAIDksjP7diAf7MD3ueekaJipvGdP0gGIBQF79yomWYMr67Bm6YVxd3T8n3fpIj3wgGfLQ4Sy3ZohQ4aoXLly+uyzz1wjgW7dumnVqlW68cYbmXmDNIk5OQAQA4YPl3LmlF54QTp61HeQbLs7dlu/fr4gKBpcdFHS820sCLLHxSrrJHflldKYMb7UuuPHpWrVpLZtpdq1vV4dIs13332n1q1ba/ny5e56zZo1NXbsWF1i+ZpAGpbuH+sjGKH27dvnerjv3btX50bDKUgA8Jg1F7C5ODt3SuefL91wg5Qtm6LGwYOSNXw6cCDpIZ6lS4drVUDk2blzpx5//HG99tpr7nrevHk1aNAgtWrVSukjNR8TCGNswE4OgNPs2CG98or0/vu+g2YrcrZ5JS1b+maLIHLlyeOr0YlW1hVt8GCpTZuE77esG9vRIMBBWnXy5Em9/vrr6t69u/766y9324MPPqiBAwe6QAeAD0EOgHiWLJEaNvTVRvgLoDdv9k1hHzVKmjrVlwIFhErr1r50u+7d/6vRseeiNU/o2lV65hmvVwh44+eff3apafNsUq7k2kNbaloNax8IIB7S1QAEHDrkq3Wwk4MJdeuyA0/bzbEdHiDUrLZo0iTrGOUb2mntn61bGpDWHDhwQH369NHw4cN14sQJZc+eXc8884w6duyoDElNAgZiCOlqAM6ITZW3VLXEWODz0Ue+dKKiRcO5MqRFNs7jppu8XgXgHTsPbd3SHn30UW2yIVKSbr75ZhfsWHtoAImjMg1AwMSJyWvfO2WKItLSpdKjj0otWvjqUqwzVbCZIwAQqX799Vc378aCGgtwSpQooa+++kqffPIJAQ6QDOzkAAg4fDjpoMAKv48cUUSxlsMPPuibL2KZG3bdPtr1Sy/1BWUFC3q9SgBI2tGjR93Mm379+unIkSPKmDGj66LWq1cvZc2a1evlAVGDnRwAARUr+upugrEqvvLlFVEee0x66y3f5/4ZK/6PNuXeJtyzowMg0s2aNUuVK1fWk08+6QKcq6++2jUbePbZZwlwgBQiyAEQ8NBDCTcc8LNUthIlpHr1FDFsHox1fUushYr9fyyNzbrCAUAk2rp1q+688041aNBAa9asUaFChfTee+9pxowZKlOmjNfLA6ISQQ6AAJs98vTTiQc4tsvz6qtJ1+2Ekw2+9O/aJMZS16ypAgBEEuuUNnr0aJUtW9YFNenSpVP79u21evVq3XHHHe46gDMTQYcqACJBnz7SuHHS+efHv/2qq6TZsyNrF8fYsNKkUuwsCLLHAUCk+OGHH1StWjUX1Fg73CpVqmjRokUaOXKka5EL4OzQeABAPHbi8JFHfIX8ixZJe/f6ZudEasbEhRcGT7Hz7+TY4wDAaxbQPPHEExozZoxrEW0BTf/+/fXII4/onKTO2ABINoIcAAmy99pq1RTx/AMiLRgLtpPzwAPhXBUAxGcBzfvvv68uXbpo27Zt7ra77rpLgwcPdjU4AFIX6WoAolqWLNILLwTfmbrrLl8raQDwgjUTaNSokWsuYAGONROYOXOm3n77bQIcRLydO3dqzpw5ijYEOQCinu3SvPyylDu377o/48PS1Nq3l157zdPlAUijDh8+rKeeekqVKlVyQU2WLFn03HPPadmyZa49NBCp1q9frxdeeEF169ZVwYIFdd1117kZTtGEdDUAMcFqiGzH5osvpD/+kPLkkW64Qcqf3+uVAUiLvvrqK9dU4LfffnPXr732Wo0aNUrFixf3emnAaU6ePKkFCxboiy++0Oeff65Vq1bFu79EiRL6888/3cdoQZADIKZS12691etVAEjLNm3apE6dOunTTz9114sWLaoRI0aoRYsWtIRGRDl06JCbxWSBzZdffqnt27cH7suQIYPq1aun66+/3l0ujMLuPQQ5AAAAZ+n48eMumOndu7cOHjzoOqV17tzZXc+RI4fXywMcqwmbNGmS262ZPn26S6n0O/fcc92O4w033KAmTZootz8HPEoR5AAAAJyF7777Tm3atNHPP//srtesWVNjx47VJZdc4vXSkMb9888/brisPw1t/vz57ja/Cy64wAU1dqldu7YyZcqkWEGQAwAAcAZ27dqlxx9/XK+++qq7njdvXg0aNEitWrVS+vT0doI3Tpw4oXnz5rmgxoKbdevWxbv/iiuucEGNpaFZU4xYTaMkyAEAAEhhkfYbb7yh7t27u0DHPPDAAxo4cKDy5cvn9fKQBh04cEDTpk1zQc3EiRMDz0tjuzPWzc+CGuuSZnViaQFBDgAAQDJZSpqlplmKmrGUNEtNsxQ1IJy2bNniGgZYYGMNBOK2eM6TJ4+aNWvmdmwaN27s6m3SGoIcAACAZJwp79u3r4YNG+bSgbJnz+6ud+zYURkzZvR6eUgDrJZmxYoVgfqahQsXxrvf2jv709Bq1arlOqSlZWn7fw8AAJDEgeWECRNcMGPtoc1NN92k4cOHq1ixYl4vDzHu77//1jfffOMCG7v8+uuv8e6vWrVqoHFA+fLlY7a+5kwQ5AAAACTABnl26NDBtdw1NsjTBnpam10gVPbt26epU6e63RobKrt79+7AfZkzZ1aDBg1cUGP1NYULF/Z0rZGMIAcAACCOY8eOaciQIXr22Wd15MgRl45mTQZ69eqlbNmyeb08xCDbJfTv1syaNcvNXfKzZhbNmzd3aWhWX2OpkkgaQQ4AAMC/vv76a7Vt29bNFjH169fXmDFjVLZsWa+XhhhLg1y2bFmgvmbx4sXx7i9VqlQgDa169epuuCxShiAHAIAYtGWL9OGHNuFcKlhQuv12qVAhr1cV2ZPgu3btqnfffdddL1iwoF544QX973//o84BqbZDOGfOnMCOzYYNGwL32XOsRo0abrfGLgTVZ48gBwCAGHLihPTYY9KIEXa2WLITwHZbt25Shw7SkCG+2+BjndLGjx/vUtH27t3rDjZtJ6dfv37KnTu318tDlNuzZ48mT57sdmvso9Xb+GXNmtWln9lujbV7LlCggKdrjTUEOQAAxJDOnaVRo3wBjjl58r/7XnzRF/BYAATpxx9/dDNvFi1aFJgEbzNvrrzySq+Xhij2+++/B3ZrbOfGOqT52Q6hNQyw3RprIECNV+ik+8eSAiOURbu5cuVyZ1bS4hAjAABSwrJfLrrovwAnIZZ59ccfUlrufmzHFU8++aSrtTl58qQ7xujfv79at25N7QNSzJ5DVlNjuzUW2Pz000/x7rfWzv75NdbyOX369J6tNdqlJDZgJwcAgBhh5SR2/GS7NYmx+995R+rZU2mOndf94IMP1KVLF23dutXdZjU3VntTiIIlpMDRo0ddFzT/js3mzZsD91kQU7t27UB9TcmSJT1da1pFkAMAQASxY6Xt2yVLzy9SJOX/NjlBTpzjsTRjzZo1ateunWbOnOmuly5d2u3kWMoQkBy7du1yc2tsx8bm2Bw4cCBwX44cOdSkSRMX1Ngcpbx583q6VhDkAAAQEb79VnrqKWn27P9uq1dPevZZqVat5H2N/Pnj1+AkxO63x6UVhw8f1oABA/T888+77lZZsmTRE088occee8wNVgSCWb9+faDN87fffutS0/yKFCni6mssFc1ajdtzC5GDmhwAADw2caLUooXv87i7MP7ykAkTpObNk/4669bZDkXyHpcWMmism1X79u3166+/uutNmzbVqFGjVKJECa+XhghlQczChQsD9TUrV66Md3+lSpUC9TXWqIL24uFFTQ4AAFHiyBHp7rt9Oyynnna0gMeOoex+SzHLmjX41ypVSrrrLum99xLe0bFUtf/9L/YDHJse36lTJ3366afu+vnnn68XX3xRN910EwelSHC3b8aMGS6wmThxopuZ5JchQwbVrVs3UF9zkXX2QFQgyAEAwEMff2yzNBK/3wIfu98ed889SX+9V17xBUfvv28HaP/dbl1sb7vNd3+ssla9I0aMUO/evV29hHVKe/TRR9WnTx/lzJnT6+Uhgmzfvt0FNLZbM23aNBfo+NkOgdXVWFBju3/MS4pOBDkAAHjoxx+ljBml48cTf4zdv3hx8oIcKzOxnZwnn5Tefluyk9IFC/p2g8qXV8yaN2+em3njb99bvXp1jRs3zqUXAf7mE7ZbY5fvv//eddvzK1asmEtDs0udOnWUKVMmT9eKs0eQAwCAhyyASU51bNxdmeSwgGbAAKWJjlc9evTQK/9uUZ133nmuycD999/PPJI07sSJEy6Y8TcOWLt2bbz7L7/88kB9TeXKlUlljDEEOQAAeKhhQ2nIkOCPsV0eexziF4i/+eabrkuaBTrmvvvu06BBg5QvXz6vlwePHDx4UNOnTw/U1+zcuTNwX8aMGV0XNAtsrCua7d4gdhHkAEAMOnRI+vxzacMGKU8eX+cum7uCyNOoka9hgDUAS2i+jXVYK15catw4dGuwIOqzz3z1Ohs3+lpMW3qbNSnInl0RZ/ny5S41zVr6mooVK7qZNzaAEWmPDXb98ssv3Y6NNRA4Yt08/mX1NM2aNXO7NTbHhm69aQctpAEgxowbJz3+uL2G+lKc7MDZDpTbtJFeeMGXHoXIsnq1VLeupV6d3kLaZgrOmSOVLRua7717t9SkibRwoe/72fe3LC/rzmbB1ddfSxdeqIhgzQSeeeYZDR061KUiZc+e3TUVsOYCdpYeaYMdulprZ3+b5wULFsS73zqg+etratWqxXMjhtBCGgDScIBjwUzcjlr+j6NG+bp0vfWWZ8tDIiyAsXr5MWOk116Tduzw7abcf7/Utq2vcUCoWMtpa35g/AGWv/207eo0bWo7J77Ax8uD2gkTJrhgZqMtStKNN97o2kKTcpQ2WOc827mzoMYuv/zyS7z7r7zyykB9je3sUV8DdnIAIEZYB9RChXw7OMEsWSJdemm4VoVItmpV8jquffWVL9jxwm+//aYOHTpo0qRJgbP0I0eOVPPkTEdFVNu/f7+mTp3qdmzs97/bth3/lTlzZjVo0MAFNVZfU6RIEU/XivBgJwcA0iCrwUkqwLH0NdspGDEiXKtCJPvii/9S1II9Z+y5Fe4g59ixYxoyZIj69evnZphYypE1GXjiiSeULVu28C4GYfPnn38GdmtmzZrlngd+efPmdcGtBTaNGzdWjhw5PF0rIhtBDgDECGsykNQBq6Wt/ZvtA7gGFZaGFuw5Y/keBw+Gc1XS7NmzXWOB1VasJKlevXqusUC5cuXCuxCEnCUU2Wwjf33Nj/7cyX+VLFkyUF9js48ypLSXOtIsnikAECOsi5q/liIxdnxgjwNMmTLBh5D6g5xQNT041bZt29StWze988477nqBAgX0wgsv6M4776TGIoYcP35cc+bMCezY/PHHH4H77PdcrVq1QH1N2bJl+d0jcmtyjh49qquuukrLli3TkiVLdGkyk8GpyQGA5LNidUtL9zcbSMzUqaFtR4zoYZ12CxeW9u5NfCCp7fTY7l8oSx6sU9pLL72kXr16ac+ePe6g1nZyLFUtD1F5TLDf6+TJk11QYx/t2M4va9asatSokQtsrN1zwVB22kBUi7ianO7du7uCMAtyAAChYd24rBPXyJEJH7DaLs5llzFUEv/JkkUaO9Y3D8dOlif0vHnuudAGOIsXL1br1q21aNGiwBT6cePGuW5ZiG62Q2NBjaWi2c6NdUjzs106axhguzUNGzakzgqpLuRBjkXr06ZN06effuo+BwCEzpAhvuYDb7wRf0aOHVtYgGMNqrxsBYzIc/vtdiZd6tZNWr/+v9utU1/fvtLDD4fm+9qZ2KeeekqjR4/WyZMn3VnZ5557zu3gnGNPWkQdSw6yoNVfX3PqyW2rqbKgxnZsqlatyu8Z0ZuuZrm1V1xxhettny9fPhUvXjxoupqltdkl7paU9b8nXQ0AUsZmrrz+up1J9dXg2IFsgwYEOEicHQ3Mn2/draR8+aRatXyBcup/n3/04YcfqnPnzm5Svbnjjjtc7U1hy51DVLHjtq+//jpQX2Pd0fzSp0/vhnFaYGOXUqVKebpWRL+ISFezF7FWrVq5LegqVaro999/T/LfDBgwQH3ttBEA4KxUqiQNG+b1KhBNLF2tevXQfo+1a9eqXbt2mjFjhrteunRpt5Nj6UqIHn/99ZebW2NBzZQpU3TgwIHAfdmzZ9c111zjdmuuvfZad5Ib8EKKg5wePXro+eefD/qYVatWuRQ1G+LUs2fPZH9te2yXLl1O28kBAADRy+bc2IlMO36wuSc2yNHm3VjNrn2OyPfLL78E6mu+/fZb1yzCz3bg/Ls1V199tbJYsRcQbelqO3bs0K5du4I+pkSJErr11lv15Zdfxmv7Z38Qln9prSDffPPNJL8X3dUAAIhudqa/ffv27iDZNGnSRKNGjdLFF1/s9dIQhNVJWTMIf33NihUr4t1/ySWXBNo8W2mCpaYBoZaS2CBkNTkbNmxwC/HbvHmz27785JNPXDvpokWLJvk1CHIAAIhOVpvRqVMn975vzj//fA0fPlw333wzc08ieMdt5syZLqixE9X+miljJ6nr1q0b2LGxOmsgTdbkXHDBBfGu58iRw320MzfJCXAAAED0sTbBI0eO1NNPP+1qNezguGPHjq7mNmfOnF4vDwlk6EycONEFNlZqcOjQocB99vtq2rSp27Gxj8wsQjQJy5wcAAAQ+77//nvXAtrfOrh69eoaO3asKleu7PXSEMeaNWsC9TXz5s1zzaL8rBba3+bZdm4yZcrk6VqBiA9yLrroonh/RAAAIDZYra41JnrllVfcdTvjb00GHnjgAWo1IoDVRM+fPz9QX2NBTlyXXXZZoL7GxnyQTohYwE4OAAA4I3by8o033nBd0nbu3Olus/ERgwYNUv78+b1eXpp28OBBTZ8+3QU2lo7m//2YjBkzqn79+oH6GjrZIhYR5AAAgBRbvny5S02zdsKmQoUKLjWtdu3aXi8tzbJGARbQWGBjs4iOHDkSuC937txq1qyZC2qswx0NnRDrCHIAAECyWTOBZ555RsOGDXNNBrJly6Y+ffq4Tmq2Q4Dw7qTZbEJ/GtqCBQvilQZYqYClodmlVq1a/H6QphDkAACAJNnBsx1MW6e0jRs3uttatGihF1988bSOqggdCyy/++67QOMA//whvyuvvDJQX1OxYkXqa5BmEeQAAICgfv/9d3Xo0MGlQvl3CKxNdPPmzb1eWpqwf/9+197ZgppJkybpr7/+Ctxn3c8aNGjgAhv7fdg8IgAEOQAAIBHHjh3TkCFD1K9fPzco0tKdunXrpieffNKlqSG0w1RtIKft2NiATvtd+J133nkuoLHdGhu07p9FCOA/BDkAAOA0s2fPdo0FVq9e7a7Xq1dPY8aMUbly5bxeWsymA/7888+B+poffvgh3v02TN2fhlazZk1lyMAhHBAMfyEAACBg27ZtbrfmnXfecdcLFCigF154QXfeeSf1Hans+PHjmjt3rgtq7GJpgX72s65WrVpgMGfZsmX5+QMpQJADAADcwMiXXnpJvXr10p49e9wB9SOPPKL+/fu74Z5IHXv37tWUKVPcjs1XX33lrvtlyZJFjRo1ckGNtXsuVKiQp2sFohlBTjL99ttvrvDPii3pLQ8AiCWLFy9W69attWjRInf98ssvdzNvqlat6vXSYsKGDRsC3dAsDdA6pPnZ0NTrrrvO7dhYgEOtE5A6CHKSacSIERo+fHhgoJYFOxdeeKG7+D/3f7SCQLaUAQCRznYRnnrqKY0ePVonT55Uzpw59dxzz6lt27Y655xzvF5eVNfXLFmyJFBfs3Tp0nj3W+qZv77mqquu4mcNhABBTjLZC1DevHm1a9cut41vL1invmj5Zc+e/bTAJ+7nBQsWJAgCAHh6EP7hhx+qc+fO2rp1q7vt9ttv19ChQ1W4cGGvlxeVjh496nZp/PU1mzZtCtyXPn161yzAghq7lC5d2tO1AmlBun/ijsaNMPv27VOuXLncmaZISRGzSc9//PFH4GJFgnE/+t8sgrGcWxucllgQVKRIEc7qAABCYu3atWrXrp1mzJjhrpcqVcrt5FiqFFLG5tVYXY0FNVZnY2ntfpZ2Zu2dbcfm2muvdWlpAMIXGxDkpLIjR4643NuEAiC7WN97SwkIxtpCFitWLNF0uKJFi7rhXwAApOT9acCAARo4cKCbuZI5c2bXZKB79+7u5BuS59dffw3U13zzzTeuYYOf7YJZfY0FNldffTU/VyCVEeREeLtI28JOLAiyACluQWJCLNXNJhonFgTZLlHWrFnD9n8CAES2qVOnut2bX375xV23HYZRo0apZMmSXi8t4tmJSZtZ46+vWb58ebz7K1asGKivqVKliktNAxAaBDlRzM4Ibdmy5bTgJ+7ndjYuKVb3c2oQFPdzKy4FAMQ2yx6wupuPP/7YXbd0aGuic8stt1AbGsThw4c1a9YsF9R8+eWX7n3Zz9LJ69SpE6ivKVGihKdrBdKSfQQ5sct+Xdu3b09wF8g+t8vBgweT/DrWAS5YEGQzEXgDBIDoZBkBtlNjndOsltR2Fzp27Ki+ffvyfpqInTt3auLEiS6wsZ2vQ4cOBe6zE4NNmzZ1QY3V1zA3CPAGQU4aZr9OK4SMG/ycGgzt3r07ya9jL+iJpcPZRyugJAgCgMgzf/58N/Nm2bJl7nq1atXczJtLL73U66VFZBMGf33NvHnz4tXMWv2rBTWWila3bl1XwwTAWwQ5SPLnmlg6nH3csWNHkl/Dan6s9iehAMg+WvEleckAED52gqtnz556+eWX3Qkv222wJgMPPvggr8dxUsIXLFgQqK9ZvXp1vPsvu+yyQBqafc7JPCCyEOTgrNgWfbAOcZs3b3ZvoMFkzJgx0CY7oZQ4O0NmXeQAAGfHXo/ffPNNPfbYYy7lyrRq1UqDBg2ibfG/72nTp08P1NfEPZFn71X16tULBDb2vgUgchHkIKSs9ejGjRsT3Q2y++K21EyInVW0QCexIMjeaEgNAIDgVqxYoTZt2rhWxqZChQouNa127dpKy7Zt2+bqa2zHxgKcuA177LiiWbNmLqhp0qSJuw4gOhDkwPOCV9vtSSwIsl0imwydFEt5C9YcIXv27GH5/wBApLEGM88884yGDh3qXnNt8GTv3r1dJzXbnUhr7FDGUs/8aWhWlxT38MbeM6y2xi4WAKbFnxEQCwhyENGssNPOsgWrC4rb1SYxefPmTbQmyC65c+cOy/8HAMLJDuStU5qdMDJ24P7iiy+61720xII7axbgbxywfv36ePfbzBp/44BLLrmE+hogBhDkIKrZU3LXrl0J1gT5P9pzIin23EksALLPLUjiTQ9AtLDXPwturK7E2GvZyJEjdd111ymtsHbY06ZNc0HNpEmT3HuFX6ZMmXT11Ve7oMZ+JjY0G0BsIchBzNuzZ0+C7bH9n/uLb4Ox9I6E0uD8n9tAVToSAYiEOkhLS7P0NBtSaU1bunXrpieffDJNpO1a+rMFdhbYzJw50/084s58s/oaC2waN27MoGsgxu0jyEFaZ/nqwYKguNOrE2NnBf1BT0JBkE0Op0McgFCaM2eO2rZtq5UrV7rrNq9lzJgxKl++vGKVHZYsX748UF+zaNGiePdffPHFLqixVLSaNWvyOgykIfsIcoDgrNNOsA5xmzZtijcULiHnnHOOihUrlmgQZPdZoAQAKbV9+3bXEvqtt95y160V9JAhQ3T33XfHZJrt8ePHXYc4f32NvRbHZQNN/fU15cqVi8mfAYCkEeQAqfCG++effwbtEGePCcbehK1DXGI1QdYm21LmAMDPTq7YME8b6rl79273OvLII4+of//+brhnLLH3+MmTJ7vA5quvvnJpyH5ZsmRRw4YNXVDTvHlzFSpUyNO1AogMBDlAGA5ELOUtWHOEuHMZEmNnZxMLguwjz3sg7ViyZIlat26thQsXuuuXXXaZm3lz1VVXKVbYCSJ/fc3s2bPjnSyy10MLaGzHplGjRmmi3ghAyhDkAB6zPyubqn1q4BP38/379yf5dawNdrA22VZ0S9oGEN3sve6pp57SqFGj3AkUK57v16+fq8WJ9noTey1cunRpoL7GArm4ypQpE6ivsZQ0SwMGgMQQ5AARzv7sLBUlWHOEv/76K8mvkyNHjqBtsgsUKEAQBETw68BHH33kBnj6m6HcdtttrpOaNTaJVtb9zHZp/IGN1Tj62euRNQuwoMYuFuQAQHIR5AAxwHZ6Tg2A4l63gapJsbz2YB3irGaIM6dA+K1bt07t27d3M19MqVKlNHr0aJemFY3spI3V1VhQY3U2cXeqrfbQ2jvbjo21e7a0NAA4EwQ5QBpg8zIsvz2xnSBrnJDUn7elwlgDhMSCoKJFiypjxoxh+z8Bsc5q9QYOHOguR48eVebMmdWrVy91797dnZSIJr/99lugG9rcuXN14sSJwH02Z8y/W9OgQQNlzZrV07UCiA0EOQBcyoiliSQWBFkL7b///jvo17BhqJY2E6xDXLQdmAFemT59uquzWb9+vbt+zTXXuDqckiVLKhpYvdCPP/4YSEP7+eef491foUKFQH3NlVdeyTBlAKmOIAdAkuysq00SPzX4iRsQ2ZnmpNgZ24Q6w/k/Wt0QkJbZ35nV3Vj9jbETB8OHD9ctt9wS8TVztvM0a9YsF9hYV7S4g5Qt1bV27dqBHRsb0gkAoUSQAyBVztraQMLEAiD7/ODBg0l+nbx58ybaIts+Wgc5IBbZTqnV2VjnNKtRsZ2Njh07qm/fvhH9nrZz505NmjTJ7dZMnTo13t+5nbRo0qSJ27G59tprXYdHAAgXghwAIWcvHdYBLlib7LjD/RJjf9sJBUD+z/PlyxfxZ7uBUy1YsMDNvLH2ycbaI9vMm0svvVSR2gjBX1/z3XffuZMcfueff77bqbHApl69eq6OCAC8QJADICLY326wNtk2Sygp1pkpWIc4m4RO7j8iqctYz5499dJLL7kTAXny5HFNBh588MGIep5aEGOBmL++ZtWqVfHur1y5cqC+5vLLL+dEA4CIQJADICpYGkywDnGW/5/US1SmTJlUrFixRJsj2FnoaB+oiMhnz9O33npLjz32WCB4v/feezV48OCIaZl86NAhzZw5M1BfY+mofvY3Yrs0Fthcd9117u8HACINQQ6AmGCND6wLXGLNEax7XNy2tQmx4mgLdBKrCbIAifQbnI2VK1eqTZs2ro2yKV++vEtNq1OnjtdLc4HMxIkT3W6NzeSx1vN+9v5qdTW2W9O0aVN3HQAiGUEOgDRT2G3zgIJ1iDt+/HiSX8eGogbrEGcpc0BCO5HPPvusXnjhBfdctOdJ7969XSc1L+dLrV69OlBf8/3338fbDbXns7++xjqj2U4oAEQLghwA+LfuYOvWrQmmwvk/xj2znRhrfhAsCOIMeNpjQYR1SrPnkLGg4cUXX/Qkzct2M+fNm+fWZJe1a9fGu79KlSouBc3WWKlSJeprAEQtghwASAZ7+bP6iWDNEex1KCnWBjuxAMg+WptdDixjgz0nLLixYMLY73jkyJEuiAinAwcOuOGitltj6Wi7du0K3Ge7M1dffbULapo3b66iRYuGdW0AECoEOQCQSqwN9qm7P3GDobgHl4nJnj17ggGQ/3MbqEoQFNmOHTumYcOG6ZlnnnEF/Fao37VrVzcDx36/4WCNOKxhgAVYM2bMiDes17q4NWvWzAU211xzjXLmzBmWNQFAOBHkAEAYz6j7A56EAiFLl0uKNT644IILEg2CihQp4hoowBvWUMAaC1iDAWMNBayxgDUYCCV7e16xYkWgzfPChQvj3V+iRIlAm+datWrRRRBAzNtHkAMAkeHIkSMJtsn2X7fGCXEHLybEDl4t5SixuiC7jwLy1GepjNYS+s0333TXrRX0kCFDdPfdd4ds580aGHzzzTeBxgG//fZbvPuvuuqqQOMAC7LYAQSQluwjyAGA6GDd36wVdmI7QdZCO6kOcXaga7s9iQVBtkuUNWvWsP2fop0Fna+88op69Ojhhnvaz/fhhx9W//79XX1VKN7rpkyZ4gKbr776yn3PuLt8jRo1coGN1ddYJ0AASKv2EeQAQGywzllWixGsTbbtFiWlQIECCTZF8AdF1HD4LF261KWmzZ8/312/9NJLNW7cOLeDkposeLX6Gtut+frrr+MFstbNzwIa262xACdcNT8AEOkIcgAgjbCXcBv4mFAAZB/tYvNckmI7FMGaI1gHuVhOjdq/f7+efvppjRgxwu3kWNBnM3DatWuXKrUu9ntatmxZoL5m8eLF8e4vXbp0oL6mevXq1GABQAIIcgAAjr3EW/pTQjtA/s/jpkclxg76gwVBVq8SjUGQ/Xw++eQTderUSZs3b3a33Xrrra6TmqUAnm1Htjlz5gQCG9u98bOfVY0aNQL1NWXKlDnr/wsAxLp9BDkAgJS81p7aECFuMGQ7RUmxmp+4HeJODYisliR9+vSKJOvXr1f79u01depUd71kyZIaPXq0GjdufFYtx62uxoKayZMnx5uzZD8ja+9sM3UsHc1SCAEAyUeQAwBINTYXxt8hLqG0ONsBSeqtJGPGjCpWrFiCQZBdrEOcPSYcrIbp+eef14ABA9ysGSvu79mzpx5//HFlyZIlxV/Pfg7+bmjWbto6pPnZDCQLamy3pkGDBjSAAICzQJADAAgbS8uyVKxgHeKsgUIwtstz/vnnJ5oOZ7tEFoycrenTp7s6m3Xr1rnrVthvuzelSpVK9tewmh2rqbGgxi4///xzvPuttbO/vqZq1aoRt4MFANGKIAcAEDFsZ8N2exLrDmcXC5SSYilvCQVA/s+DdSGz79+lSxd9+OGHga81fPhwtWzZMlm1RLb7Y13QLKixrmj++h1jQUzt2rVdYGO7Npb2BgBIfQQ5AICoYTsj27ZtC9ocwVLmkmKtlxMalGrtoMeMGaMDBw64gKRDhw565plnknxf2bVrlyZNmuRS0WyOTdwudTly5FCTJk3cbs21116rvHnzpsrPAgCQOIIcAEDMsLcpCzgSCn78H+19IinWlrlEiRIqV65cgs0RLFD55ZdfAt3Qvv32WxeA+Vm3NX83tPr166dK+hwAIPkIcgAAaYp1NfMHQKtWrdJ7772nn376yd1n6WjJeatL6HFWC2QNA6yttNXvML8GALxDkAMASHPs7eztt99Wt27dtGPHDnfbPffco8GDB7t6HX8QZE0HrAvakiVLXNe4uN3QgsmUKVOibbLtYo0TUmNwKADg7GMDXo0BAFFv5cqVatu2rRu+aSwlbezYsapbt667brN+rDbH0tCmTZumw4cPB/6tvVHa/Jrq1aurePHiCabGbdq0yTVHsNk6dkmI7fJYDVBiQZC10CbFDQDCgyAHABC1rCHBs88+qyFDhrgdGZtD8/TTT7tOar/++qsGDRrkamy+//77eKlotiNj9TV2sUDIdmmCsa/9559/JtocwXaEjh8/HrgtsXQ46+qW2MBUW1O2bNlS/WcEAGkR6WoAgKhkrZytU5o/qGjevLlLT1u4cKHbsVm7dm28x19++eWB+TWVK1dOVuvo5LIGBVu2bEm0O5x9tDbUSSlQoECibbLtY86cOVNtzQAQbSKmJsdekE89o2UTpnv06JGsf0+QAwA4le2adOzY0e3Q+FtHV6pUyTUa2LlzZ+BxGTNm1NVXXx3YsbFUMq/YW63VCSUUAPk/379/f5JfJ0+ePEGDILs/NYM3AIgkERXkPPDAA3rooYcCt9lZqGAD2+IiyAEA+Fk62LBhw9SnTx9XU2MH8zb35sSJE4HH5M6dW82aNXM7NlZnEy3vHfZWbB3iggVBf/31V5Jfx+b3BAuCbKeIIAhAtIqoxgMW1BQqVCjU3wYAEONd0x577DHXQCDu7RbgWLMA//yaWrVquR2caGOBh+3C2OWyyy5L8DG20+MPehIKgmygqg08XbFihbskJEuWLPE6xJ0aDFnNEG2yAcSCkO/kWA6ynX2zF9X//e9/6ty5c6ItNo8ePeoucaM160bDTg4ApC1W6G/DOD/44AM38+bUVK6qVasGApsKFSqwOyG53S1L5UtsYKo1TkjqLd/en+39OqFdIPtoKX/RGEQCiA0Rs5NjOdNW6Hneeedp3rx56tmzpyvMHDp0aIKPt3qdvn37hnJJAIAIZYHMlClTXNOASZMmaffu3fHut5NedqLstttuU5EiRTxbZ6SyznJlypRxl4RYC2xrhZ1YELRx40YXXFpXOrskxNIDbR5QQi2y/R3ibLcIAKJuJ8eaBjz//PNBH2PTpsuWLXva7a+99poeeeQRt52e0KwAdnIAIG2xg27rkmZNBL7++mt3IH4qO3h+9dVXXRMBhI6l/m3evDnRDnF2ifsenZiCBQsmWhNkH61uCAAirvGAdYexQWnBlChRIsGZA5YjXLFiRa1evTrRM01x0XgAAGKLveUsW7bM7dZYYLN48eJ491vjAHvNt8dZk5p+/fqpffv2iaY5I3ysTbbVRAVrk33w4MEkv45ld5wa+MT9aM8BAAh7ulr+/Pnd5UwsXbrUbXVbdxcAQNpguzNz5851QY0FN1Y34me1NNWrV3fNA6ZPnx5oLNCyZUvXSc1SoxAZ7P3bGgnZ5aqrrjrtfgtMrQNcsCDIOsjZY+xyaoDrZwcuwTrEWctwarAAeNZ4wKZLL1iwQPXr13cd1uy65VI3bdpUb775ZrK+Bjs5ABCd7GB28uTJLqixj/Y6Hrd2pHHjxq5xgDUN6N27t6ZOneruu/jiizV69GjX/hmxx97XE6oH8n9u2SJJsedPQrtA/s8tCLOADEDsiYg5OXaGpm3bti41zXJ47Szd3XffrS5duiRYj5MQghwAiB52kOqvr5kzZ44rYvezHfzrrrvOdUNr0KCBa1Ns9Z39+/d37xGW4mw1n3axg1ikTYcOHXI7fYkFQda8KKnDFnsuWT1vYjtBtjtI+iMQnSIiyEkNBDkAELns7ePHH38M1Nf89NNP8e4vV66cC2psx8bSm/xn12fMmOFOgq1bt85db9Sokdu9KVWqlCf/D0QPC4itC1xiKXHWyCLucNiEWIBtgU5CQZBdrENcck/GAggvghwAQMgOMq0LmgU1tmtjs1f8LIixYZwW1Njl1KDFzsLbbr7NvjE2eNLqbm699VZqLJAqbPfQnpPBOsTZ7L6k2HMzsSDILtYUA0D4EeQAAFKNddT86quvXGBjtTM2BsDPDvaaNGnigppmzZopb968p/17O7M+ZswYPfnkk+513YIh65j2zDPPuNd4IJwd4rZu3RoIfOIGP/7bbKhqUqz5QbA22TyvgdAgyAEAnJVffvklkIb27bffxksBsrPcFtRYKpo1lwk2/HHhwoVq3bq1lixZ4q5XrVpVY8eOdYOigUhjh0Q7d+4M2iHOjk2SYm2wEwuA7GInA9i9BFKOIAcAkOIz3IsWLXJBjV1WrlwZ7/5KlSoFAhsLUJLqXrV792716tVL48ePdweOdtA3YMAAPfTQQ64mAojmzoEJBT/+j0nNEvTvgAZrk22NOugQB5yOIAcAkCRLy5k5c6bbsbH6Gkvj8bNApG7dui6osa5o1iEzOewt5Z133lG3bt0CM2+ss+bgwYNVsGDBkP1fgEhh6ZzBOsTF/TtLjDU+sAYIidUFWeMEThYgLdpHkAMASIjNIZk0aZLbrZk2bZpr2etnr7M2y8x2bOxjnjx5UvS1V61a5bqmzZ49O9BdzWpx6tWrl+r/DyBaHTlyxAVBiaXEWeME21kNxlpgFy1aNNFZQXaftdIGYg1BDgAgYO3atS6osR2befPmxTuAsnki/jQ027k5kwMjC5T69eunIUOGuM5VNufmqaeeUteuXTnQAlLI/oasFXZi6XDWQjvuDKqEWL2P7fYklhJnu0TMo0I0IsgBgDTMmgTMnz8/0DhgzZo18e63mhp/YFO5cuWzKoCeOHGi65RmB2CmefPmGjFiRLLT2wCk/O/b2rEn1iLbLrZblBRLH00oFc7/ec6cOcPy/wFSgiAHANKYgwcPavr06S6wscDD0tL8MmbM6Lqg+etrbPfmbFm6zaOPPqoJEya46/Y1Lbix70HXKMA7dlhn9XCJBUH2edw28Ik577zzggZBls7K3zrCjSAHANIAK2C2gMZ2a2bMmBHv7K11M7O5NbZjY3NsUus11FJpbIBn3759XZqa1QbYgM+nn36aAYlAFLDDPut+GCwIsvuTYjs9wdpkW4c4giCkNoIcAIhB9nJtxf3++poFCxa42/zsAMN2UuxSq1Ytt4OTmr755hu1adNGK1ascNdr167tGgtUrFgxVb8PAO+Pv04dkhr3o79zYjBW8xOsQ5zN26JDHFKKIAcAYoQVGH/33XeB+hob0hnXlVde6YIa27GxYCMUZ04t9a179+564403AtPerSX0vffey5laIA2yXdxgHeI2b94c7wRMQuwkjKW5JjYryBonpPaJGkQ/ghwAiGL79+/X1KlTXWBj7Z7/+uuvePMzGjRo4IIaq68pUqRIyNZhXdheffVVPf7444H0FRvmaUM9bWI7ACTk2LFjrgtcYkGQ3WcNFIKxYagW6CTWJtt2iez1EGnLPoIcAIguNhvDBnLabs2sWbPcQULcAmDrWmY7No0bN1aOHDlCvp5ly5a51LTvv//eXbcubGPHjlX16tVD/r0BxP4Ote32JJYOZ7tER48eTfLrWMpbsLog6gRjD0EOAEQ4e+n96aef3G6NXX744Yd495csWTKQhlajRg1X4B+uXaTevXu7Tml2ptUCqmeeeUYdOnQI2xoApG22i7xt27ZEd4LsY9xBxomx1NrEusPZxRq0ILoQ5ABABLLOZHPnzg00DvDPljFW21KtWrVAYFO2bNmw1rvYW8Enn3yiTp06uTOs5pZbbtHw4cNdyggARAp7vdq1a1eCu0D+z+3YMSl2jBmsTbYFSdQdRhaCHACIEPb6NXnyZBfUfPXVV/HeeK37UKNGjVxQY+loNpzPC9bMoF27dq4OyJQoUUKjR492racBIBrZa22wnaCdO3cm+TWyZcsWNAgqVKiQqx1C+BDkAICH7E3UX18ze/Zsl3/ulz9/ftcwwHZsGjZs6N5EvWI5788//7z69+/vPs+UKZN69OjhLhaAAUAsD1C22p/EgqAtW7Yk+TXsNdMaICRWE2S74KT5pi6CHAAII3sZXbx4caDNsxXtx1WuXDm3W2OXq666KiJmQ9jw0LZt22rdunXuunVss5k3pUuX9nppAOA5O/FjXeBOTYfzf9y0aZOrHQrGXuuLFi2aaIc4a6FtgRKSjyAHAMLwBmi7NP76GuuO5mfpCzVr1gzU15QqVUqRws5OdunSRR988IG7bukWw4YN02233UbuOQCkoMbSXvfj7gSd2iHOHhOMvebaGIDEUuJsl8jL3f5IRJADACFg82qsrsYCmylTpujAgQOB+6xV6TXXXOOCmmbNmrmC1UhindJsp+bJJ590r60WiFkdzrPPPuteZwEAqfuau3Xr1qDNEY4cOZLk17EU58S6w9nnae34eB9BDgCkjl9//TWwW/PNN9/EG2BnMxr8aWhXX321smTJoki0aNEitW7d2qXUmSuvvNLNvLniiiu8XhoApEl2+L1jx45Ea4Lso7X0T4q1wU6sJuiiiy5yc9ZiaZeeIAcAzpDlWFtQ4K+vWbFiRbz7L7nkEhfUWCqaBQmR3Flnz5496tWrl8aNG+feUO31dMCAAXr44Ycjoi4IAJAwe8221/DEdoHso2UXJCV79uwJ7gT5P1pXz2gKgghyACAFDh8+rJkzZ7rAxrqiWYqBnwUDderUcUGNdUWz9sqRzl7W3333XXXt2lXbt293t911110aMmSIZ22qAQCpy3Z6EqoH8n++bdu2JL+GZSAE6xBnNUORdFKMIAcAkmBpApMmTXK7NdOmTYs3PTtnzpxq2rSpC2zsY548eRQtVq9e7bqmff311+56mTJlXGpa/fr1vV4aACCMjhw5kmibbLtY44SkOsRZC2zrAmeBz0cffeR5vSlBDgAkYO3atYH6mnnz5sV7cbcXcX99Tb169aKuracFac8995wGDx7sOvrY2bmnnnpK3bp1i7r/CwAg9I4fP+5aYSdWF2QttP1z3iylzYImr99PUhIbMKEIQMyyJgHz588P1NesWbMm3v2XXnqp262xi30eTXnJcU2cOFEdOnRwb0rGuruNHDlSxYsX93ppAIAIlTFjRvc+kdh7hb2H2tgBe2+xNG6vA5yUIsgBEFNsR2P69OkuqLGDf0tLi/uCbrs0/voay0OOZpaG8Oijj2rChAnuug2ds+DG/n/RGrABACLDOf8OM7VLNCLIARD1rLjSAhoLbCzAiTt7wLa1bWfD0tCaNGkSEzNhLMVg+PDh6tu3rw4ePOhypjt37qynn35aOXLk8Hp5AAB4jiAHQNSxUkIrsPfX11hKWtzyQusI409Dq127ttvBiRXffvut2rRpo+XLl7vrtWrVco0FKlas6PXSAACIGAQ5AKKCFT9aswB/YLN+/fp491epUsUFNbZjY7NsYi1da+fOnerevbtef/11dz1v3ryuycC9994b0bN6AADwAkEOgIh14MABTZ061QU11u55165dgfusALJBgwYuqLH6mvPPP1+xyDrAvfbaa3r88ccDg98efPBBDRw40AU6AADgdAQ5ACLK5s2bXVBjFxvQeezYscB95513nquvsR2bxo0bu3k2seynn35yqWm2g2UqVarkUtNq1Kjh9dIAAIhoBDkAPGW1ND///HOgzfMPP/wQ7/6LL744kIZWs2ZNV2SfFqZY9+nTRy+++KJr4WnNBKzJQMeOHdPE/x8AgLPFuyUAT7qDffPNN4H6Gv98F2O1NFdddZULaiy4KVeuXMzV1wQL+P7v//7PtYW2SdTmlltu0bBhw6K2hScAAF4gyAEQFjadeMqUKS6o+eqrr7Rnz57AfVmyZFHDhg1dUNO8eXMVKlRIac2vv/6q9u3ba/Lkye56iRIlNGrUKDVt2tTrpQEAEHUIcgCEdFilv75m9uzZbgfHL3/+/C6gscDGApzs2bMrLTp69Kjrkvbcc8+5+T7WUMGaDPTs2VNZs2b1enkAAEQlghwAqZputWTJkkB9zdKlS+PdX6ZMmUB9TbVq1dw05bTMGiu0a9dOa9ascdetW9zo0aPdzwkAAJw5ghwAZ70TYbs0/h2bTZs2Be6z+S3WCcwCG2vzzMG7z9atW9W1a1e999577rql5w0dOlS33357mqk/AgAglAhyAKTY7t27XV2N7dZYnY11A/PLli2brrnmGrdbY+2eLS0NPtYpbdy4cXriiSdcjZIFgW3btlW/fv2UK1cur5cHAEDMIMgBkCy//fZboBva3Llz3QG7n+1E2E6N7dhcffXV1JIkwFpj28wbf4vsKlWquIDniiuu8HppAADEHIIcAAk6efKkOyD319csX7483v0VK1Z0uzV2ufLKK92uBE5nXeSefPJJjRkzxtUs2Y5N//799cgjj6T5miQAAEKFIAdAgHX3mjVrlgtqvvzyS23ZsiVwnx2Q165dO1BfY0M6kTgLaKzmxmpvtm3b5m678847NWTIkDTZIhsAgHAiyAHSuJ07d2rSpElux2bq1Kk6ePBg4L4cOXK4OS22W3PttdfqvPPO83St0WL16tWua5oFjMYaLthOjqXyAQCA0CPIAdKgdevWBeprvvvuO5ea5le0aNFAfU29evWUOXNmT9caTQ4fPuzm3QwaNMjNBLIhp0899ZTbzeHnCABA+BDkAGmANQlYsGBBoL7GdhriuvTSS91ujQU2l112GW2Mz4B1m2vfvr1r0GBs52vUqFEqXry410sDACDNIcgBYtShQ4c0Y8YMF9RMnDhR27dvD9yXIUMGt0vjr6+58MILPV1rNNu4caM6deqk//u//wvshI0YMUItWrQgWAQAwCMEOUAMsQJ3C2hsx2b69OkufcrPunrZ7oLt2FidDXNZzo6lo1kw07t3b1fHZI0ZOnfu7K5bLRMAAPAOQQ4Q5R28LPXMn4Y2f/58d5uf7dD42zzXqVNHmTJl8nS9scLqmGzmzc8//+yu16xZU2PHjtUll1zi9dIAAABBDhCd9TXz5s0LNA6wJgJx2XBJS0OzwKZSpUqkTKVyJ7rHH39cr732mrueN29e12SgVatWzAkCACCCEOQAUeDAgQOaNm2aC2osHW3Xrl2B+2x3xloTW2DTvHlzVxOC1GXd515//XUX4Ph/9g8++KAGDhzoAh0AABBZCHKACGWDOG0gp+3YzJw5U0ePHg3cZ/NqmjVr5nZrrrnmGuXMmdPTtcYyS0lr3bq12z0zlpJmqWmWogYAACITQQ4QIayWZsWKFYE0tIULF8a7v0SJEm63xi52gG0d0hDa3bM+ffpo+PDhLkUwe/bseuaZZ9ShQwdlzJjR6+UBAIAgOEoCPO7Q9e233wYCG/+MFb9q1aoF5teUK1eO+powBZufffaZHn30UW3atMnddvPNN7tgh1RAAACiA0EOEGb79u3TlClTXFBjAyR3794duC9Llixq2LBhoL6mUKFCnq41rfn111/dTo39XowN8hw9erRruQ0AAKIHQQ4QpoGRFtTY5euvv3Y7OH758uVzAzltx6ZRo0YuLQrhZfVOQ4YMUb9+/XTkyBGXjmZNBnr16qWsWbN6vTwAAJBCBDlAiFKeli5dGphfs2TJknj3lylTJpCGZilpNkgS3pg1a5batm2rNWvWuOvWqW7MmDHudwQAAKITQQ6QSo4dO6Y5c+YE6mts98bPamlq1KgRmF/DAbT3tm7dqm7duundd9911wsWLKihQ4fqjjvuiOzap0WLpJEjpa+/9l2vXVvq0EGqXt3rlQEAEDEIcoCzsGfPHle/YUHN5MmTXb2NX7Zs2dS4cWMX1Fh9Tf78+T1dK3ysU9r48eNdKtrevXtdQNOuXTs9++yzyp07tyLa4MFS9+6Sddb7+2/fbR9/LL3/vtS3r/T0016vEACAiECQA6TQ77//HtitmTt3rv72H2z+uxtg9TW2Y9OgQQPqOSLMDz/8oDZt2riP5oorrtC4ceNUpUoVRbwpU3wBjonznAt83ru3VLGidNNN3qwPAIAIQpADJGPa/eLFi11gYxcbDhlX+fLlA2loVatWVfr06T1bKxLfcXvyySddrY3VS+XKlUv9+/fXI488Er56qKVLpXHjpMWLrY2e1KyZdP/9UnJ3+AYNkmytJ04kfL8972ynhyAHAACl+8fe8UNk0qRJbnjeTz/95Frj1q1bVxMmTEj2v7fUHzsYsZSSc889N1TLBE5jHbasC5oFNV9++aU2b94cuM+CmFq1agUCm5IlS3q6ViTOXt7ef/99denSRdu2bXO33Xnnna6TWtjac9tLbK9e0sCB8dPMLCixYOfLL63bQfCvcfSo77HJsWePlCvX2a8bgLcOHvS9PthrV8GC0nXXSXTfRBq3LwWxQch2cj799FM99NBD7mypdSuylJ7ly5eH6tsBZ23Xrl0uMLc0NJtjc9DeYP6VI0cONWnSxAU11157rfLmzevpWpE065ZmtTYzZ850163Zg+3k2OtRWL30ki/AOTXN7ORJi6al5s2llSuliy5K/GvEaTmeJAuIAEQvOzHywgu+OrsDB3wnROz1wgIcq7t77DHrZuP1KoG0uZNjAc1FF12kvn376oEHHjjjr8NODkJt/fr1gTbP3377rUtN8ytSpEigzXO9evXcbiQi3+HDh93JlUGDBrmOd/Z7s1Q166SWOXPm8C7Gnk8WvMTptHcaS0Hr0sWXjpYYe5kuVkz688/g3y9fPmsb5/uaAKJTv37SU08lfn+fPr4aPCAN2uf1To7VL/z5558ureeyyy5zrVovvfRSDR48WBWtMDbIQD67+MXtVAWkBgtiFixYEAhsVq1aFe/+SpUqBdLQrCg9olsJ4zTW6a59+/b67bff3HXbdRs5cqRKlCjhzYJ++il4gGOsxubDD4MHOfY8bNdOevJJX+CUEAts2rQhwAGi2fbtvh2cpIKg1q19KWwAwhvk/Prrr+5jnz593NwJ29V54YUX3NnwtWvX6rzzzkvw3w0YMMDt/gCp6dChQy5lyYKaiRMnBmozTIYMGVytmAU1drHnKqLPpk2b1KlTJ5cma4oWLaoXX3xRN954o7eBqqWapNbjHn1U+uQTadmy05sPWGBTrpzUrduZrRNAZLC5XYmdyPCz+995R+raNVyrAqJSitpA9ejRwx0wBLusXr06kPLzxBNP6Oabb3ZnxF9//XV3/8c20yERPXv2dNtP/kvcYYpASmzfvl2vvfaaWrRooXz58rkA5tVXX3UBjm1v3nbbbXrvvffc42bMmKGOHTsS4EQhS421Eyhly5Z1AY51SuvatatWrlypm266yfuduOLFk86dt3z70qWT/lrZskmzZ0uPPBK/CYGl4N13n/TNNxJpvUB0+/33pHdj7X57HIDU28mxg4dWrVoFfYylhWzZsiXQWtfPcuHtvg0bNiT6b+0xYc+ZR8ywANufhvb999+7zlp+F1xwQaC+pk6dOsqUKZOna8XZmzdvnlq3bh1o6V2jRg2NHTvWpRxGjPPPl5o2laZOTbz1s50UsjSz5MiZUxo92ra9fS2p7TleubIU6UNMASSP/S0nVSpt99NBEUjdIMcmtidnarvt3FiwYt2NrNWuOX78uBuieOGFF6bkWwJBJ9dbMOMfzGmpkHFdfvnlgfqaypUre39WH6nWBe/xxx93O3PG0l+tycB9990XmTOKbHbN3LnWEeH0QMfWW6OGdPvtKfuatmNTp06qLhNABGjZUnrmmeCPsS6Nt94arhUBUSskNTmWDmRnWHv37q1ixYq5wMaaDpiW9gcMnCFr6zxt2jQX2Fi75507dwbuy5gxo2sPbEHNdddd5557iB2WBvvGG2+oe/fuLtAx999/v55//nmXkhixbEd73jzpoYekBQv+u91m5txzjzRihMTOInA66xRoc2KsZu3ii327ohkzKqZZcyZrKz95csK7v5aq1rixdcnxYnVAVAnZnBwLaqyo++6773YtXa+66irNmjVLefLkCdW3RIyy9EcbyGm7NVY/E7cDX+7cudWsWTO3Y3PNNdfQajxGWUpamzZt9N1337nr1qVx3LhxqlmzpqLCJZdI8+f7uq1Zep0FNfXq2fa41ysDIo/Nj+rQQXr9dd+Bvh3Y28cCBXzpmrfcopj23nvSDTdIX3/93wBh/0fLjvngA69XCKTdOTmphTk5aZM9JVesWBGor1m4cGG8+4sXL+6CGrvYQa7t4CA2HThwwHVcHDZsmEtPzJ49u+va+Oijj/J7D6dDh6SJE/+bvG5nmq0RApDa7JDk+uutH3zCXcYs7di6KN54o2L+52CNRt56y870SYUK+XZ+69dnECjStH0piA0IchAxXbJsGKe/vsbfhtyvatWqgfqaChUqUF8T4+xlacKECS6Y8XdZtG5pw4cPJw0xnOztYehQ39yO/fv/m7xuDRBsGKENMeVvEalp5kypYcPE77fnm70G2CysSKzBAxDbw0CB5Ni/f7+mTJnighqrr9m9e3fgPmtc0bBhQxfYNG/eXIULF/Z0rQgfG+TZoUMH95zw79yNGjXKDfZEmD33XPzJ6/4z6xbw2Ewea6ZgA0qB1PLKK/+lZiUWeFuX1jlzfLsaAJAIghyEfWij1dfYjs3XX3+tY8eOBe7LmzevaxhguzWNGzd2qUlIO+y5MGTIEPXr18/V8Vk6mjUZ6NWrl7KRGhWZk9ft/ocf9tVKAKnBdvETC3Di+uOPcKwGQBQjyEHI046WLVsWqK9ZvHhxvPtLlSoVqK+pXr26G+aItMcC3rZt27pZR6Z+/foaM2aMG/IJj9hE9eROXre0NSA15M37X1pkMMyGApAEghyE5Iz83LlzA/U1cQfAWi2NDW203Rq7cBCbtm3bts0NGX733Xfd9QIFCmjo0KH63//+R91VpExeD3awyeR1pDabGWXtk4PJkcPXRhkAgiDIQarYs2ePJk+e7AIb+2iFYX5Zs2Z16We2W2Ptnu1AFmmbdUobP368S0Wz4kELaKxF9HPPPefagiMCWEEnk9cRbjbksk8fyRqOJJa21r073f0AJIkgB2fs999/D9TXzJkzx3VI8ytYsGCgvsYaCFigA5gff/zRBTSLFi1y16+44gqNHTtWV155pddLQ1w2uNkaDwRjf/OpMeDZZl9ZjYW1Bb/wQrpmpWVZsvg6rNlOzfr1/83I8TcjaN9eeuIJr1cJIAoQ5CBF9TV2gOqvr/nJBhvGUb58+UCbZ2v5nJ4DFcRhOzZPPvmkq7U5efKka/3Yv39/tW7dmlqsSFS5sm/C/LRpoZu8bju+zz4rvfyyPUF8t110kdS1q9S2bfBgx9Y0dapkc7TscXXrSnXq0NI6FhQvLq1cKX35pW8mjnXzK1lSevBBe6PxenUAogRzchDU0aNHNWvWLBfY2GXz5s2B+yyIqV27dqC+pqS9CQGnsJeYDz74QF26dNHWrVvdbVZz88ILL6iQDbhD5LLAwyavW7veUyevW1DxxRe+tLYz/do2vX3VqvhBlAUp9rZ0772+ifcJBS0LFvjSmqzez9ZjbE12AGwHxdT6AUBMYhgozsquXbv01Vdfud2aqVOnuqnzftbWuUmTJm7HxuaWWNtnIDFr1qxRu3btNNPSTySVLl3a7eQ0aNDA66Uhuewtwn5/NnndglT/5HX7HZ7NrkmnTtKoUQnvEvlZwHLTTfFvszP8ltpoKW6n/lsLePLkkZYulYoUOfO1AQAiEkEOUuyXX35xQY1dvv32W5dO5FekSJHAbo219s1iOdNAEDbnZsCAAXr++eddtz17zjzxxBN67LHH3KBXpHGHDlnhnhTnBEqC6XC1a1t/8fi333GH9MkniRel27+zltaDBilm7dwpjR8vvfaab56RNXO5/36pdWtfC2YAiFEEOUiSBTELFy4M1NestLOjcVSqVMkFNbZjc/nll1Nfg2Sz7nrt27fXrzbUT1bW0VSjRo1SiRIlvF4aIoXV81nNT1JsIHDcQMg+t52apIZF2mP++ksxad06X+2RBTdx23vba7QFjnPn+upXACAGpSQ2oPFAGju7bmlDFtRYVzSbUeKXIUMG1a1b1wU21hWtuBV+AimwadMmderUSZ9aipGk888/Xy+++KJuuukmZt4gvuQ2mjj1cbaDkVSAY3bvlo4f93VriyUW1Fx3ne/ncOr8Irtugc/110srVtCAAUCaR5AT43bs2KGJEye6HZtp06bpkKWJ/MsiYDvLbrs1VmeTx85+AilkrcNHjBih3r17u/ot65T26KOPqk+fPsqZM6fXy0MkKl3al2JlB+WJsfqahg3j32YzlGzHItiAUv8OkL8hQSyx2qg1axK/32qUrJHDrFm+mikASMNi8F0Aa9euDdTXzJs3z3W38rvgggsC9TW2c5MpUyZP14roZs8vm3njbydevXp1N/OmcnJSkZB22Q5Lhw7S008nPnDUdmweffT0IMfaWk+ZknjDAgturDFCLOxk2G6Utdm2kwX2Wm3tvO1nZ7cnxv7/1lqbIAdAGkeQEyPT4+fPn++CGtuxsY5WcVlNjX9+jR18kjqE1OjA16NHD73yyivu+nnnneeaDNx///3UbyF5Hn/cN+PGZqHE3Z3xD38cONBXe3Kqp57yHcT7W03HZV/HGltY44For7ux//+77/q6yFlgc/vtSe9gGfu5HDsWjlUCQEQjyIlSBw8e1PTp011QY+lolpbmlzFjRtcFzQIbq68pVqyYp2tFbDWseOONN9S9e3cX6Jj77rtPgwYNUr58+bxeHqKJHbj/3/9J77wjjRjha0bgT1Hr3DnxnYirrpI++8zXZe3gwf/qdmzn57zzpM8/j+7C+0WLpKuvlo4c+a/+yHZu3n/fF8AE28XxP/ayy8KyVACIZHRXiyI2SNEaBlhgM2PGDB2xN8F/5c6dW82aNXOBzTXXXMPPC6nu559/dqlp3333nbtesWJFl5pWywY6AmfL3opSsstsndZsp8MGg1qgY8NJW7b07eREK9vBuugiacuWhNPxbKfKfk6JvW3bz89e++3fZ80a8uUCQLjRXS1GWPxprZ39bZ4X2Jt5HNYBzZ+GZgeatoMDpDZrJtC3b18NGzbMpUbaQFhrKmDNBXjOIdWkNI02Rw7pkUd8l1gxaZK1KUz8fn+6mv2s7HJqC2nzxhsEOABAkBOZnarsTLm/vsaGdMZVtWrVQOMAO5NOfQ1CGWRPmDDBBTMbN250t914442uLTQpkEAIzJuXdGMBu//aa611pu/xftWrS88+K9WvH5alAkCkI8iJAPv379fUqVNdYDNp0iTtthkP/7Lp8A0aNHA7Ns2bN1eRIkU8XSvSht9++00dOnRwz0dz0UUXaeTIke45CMBjRYtKEyZIv//ua8NtQ0AvvNDrVQFARCHI8ciff/7pdmrsMmvWLB2L0w0nb9687mDSdmsaN26sHJaWAYSBPQ+HDBmifv36ueGxlo722GOP6YknnlC2bNm8Xh4Q22rWlJ5/PvhjbJfHHmesfscuAIDTEOSEMfXHZon462t+/PHHePeXLFnS7dbYxWaNZIjFQXaIaF9//bXatm2r1atXu+v16tXTmDFjVK5cOa+XBqQNloZmqaCbNyfeeCBvXunmm71YHQBEFY6kQ+j48eOaO3duoL7mjz/+CNxntTQWzPjra8qWLUt9DTyxbds2de3aVe9apyrZIPoCeuGFF3TnnXfynATCybrEffrp6S2kjZ348rfdZogzACSJICeV7dmzR1OmTHGBzeTJk12LO7+sWbOqUaNGbrfG2j0XtDxqwCPWKW38+PHq1auXe55aQNO6dWs999xzypMnj9fLA9KmK6+UFi/2pa3ZDCH/MND//U/q3l0qX97rFQJAVGBOTiqwHRp/fc3s2bNdhzQ/OytuAzltt6Zhw4bUNSAiWLqkzbxZZIMHJV1++eVu5o117wMQIaz+Zt8+KWdOdm8AQMzJCTmLCxcvXhxIQ1u2bFm8+62GwYIa27Gxg8Zz/BO5AY/Zi8KTTz7pam1OnjzpXiBs58YCHp6nQISxHRyrwQEApBhBTjIdPXrUFWb7d2ysO5pf+vTp3TBOf31NqVKlPF0rkFBg/sEHH6hLly7aunWru+2OO+5wtTeFCxf2enkAAACpiiAnmWwIotXY+NnU92uuucbt1lx77bXKly+fp+sDErN27Vq1a9dOM2bMcNdLly6t0aNHu/RJAACAWESQk0w2r2bp0qWBNLT69esrS5YsXi8LSJTNuRkwYICef/55N//GBsvavJvu3bu7zwEAAGIVjQdSkK5mgxEtNQ2IdNbhr3379vrll1/c9SZNmmjUqFG6+OKLvV4aAABAyGMDjtiTyc58E+Ag0lmtWMuWLdW0aVMX4BQpUkQff/yxvvrqKwIcAACQZnDUDsQAa1s+bNgwN1T2k08+cZ3SOnfurNWrV+uWW25hqCcAAEhTqMkBotz333/vWkD7W5lXr17dzbypXLmy10sDAADwBDs5QJTatWuXHnroIdWoUcMFOHny5NFLL72kb7/9lgAHAACkaezkAFHGeoW88cYbrkvazp073W2tWrXSoEGDlD9/fq+XBwAA4DmCHCCKLF++3KWm2W6NqVChgktNq127ttdLAwAAiBikqwFR4MCBA27n5rLLLnMBTrZs2dzOzZIlSwhwAAAATsFODhDhqWmff/65OnbsqI0bN7rbWrRooRdffFEXXHCB18sDAACISAQ5QIT6/fff1aFDB02cONFdv+iiizRy5Eg1b97c66UBAABENNLVgAhz7NgxDRgwQOXLl3cBTsaMGdWzZ0+tWLGCAAcAACAZ2MkBIsjs2bPVtm1brVq1yl2vV6+exowZo3Llynm9NAAAgKjBTg4QAbZv36577rlH9evXdwGOtYJ+6623NGvWLAIcAACAFCLIATx08uRJjRs3TmXKlNHbb7+tdOnSqXXr1lqzZo3uvvtudx2AB/7+W9q6Vdq92+uVAADOAEEO4BFr/1y9enU392bPnj2uPfT8+fPd3Js8efJ4vTwgbdq3T3rySalQIalwYem886SqVaVPPvF6ZQCAFCDIAcJs3759evTRR1WlShUtXLhQOXPmdC2h7fOqdjAFwBu2a1OjhjRwoLRr13+3//ij1LKl1Lu3l6sDAKQAQQ4Qxpk3H374ocqWLasRI0a4VLXbb7/dpabZHJwMGegDAnjq8cel1aulEyfi337ypO/jM89I8+Z5sjQAQMoQ5ABhsG7dOl1zzTUuqNmyZYtKlSqladOm6f3331dhS4kB4K09e6S33jo9wInLTkSMHBnOVQEAzhBBDhBCR44cUZ8+fXTJJZdo+vTpypw5s/r27auffvpJjRo18np5APx++kk6ejTpZgTffBOuFQEAzgL5MUCITJ06Ve3atdMvv/zirjdu3FijR49WyZIlvV4agFMlt5MhHQ8BICqwkwOksj///FO33nqrmjRp4gKcIkWK6KOPPtKUKVMIcIBIdemlUtaswR9j6WpXXx2uFQEAzgJBDpBK/v77bw0fPtwN7/z444+VPn16derUyQ33bNmyJTNvgEiWM6f0wAPSOecET1dr3z6cqwIAnCHS1YBUYPNtbN7N0qVL3fVq1aq5eTeX2tlhAN6wTmmzZ/uaCVx5pe8S7GTDgAHSggW+ltH+jmrGAh/7GoMH+74GACDisZMDnIW//vpLjzzyiGrUqOECHBviOX78eH333XcEOIBXtmyRrLFHuXJS27ZShw7SVVdJl18urViR+L/LkcMXFFmwc8EFvtssKKpfX5oyRerWLWz/BQDA2Un3jw3viOChibly5dLevXt17rnner0cIMD+bN566y099thj2rFjh7utVatWGjRokPLnz+/18oC0PdCzShVpwwZfellctiNjgcwPP0hJ1cfZW+Phw1LGjL4Lkv552W4X874AREhswE4OkEIrVqxQ3bp1XVBjAU6FChU0d+5cvf766wQ4gNdGj5Z+//30AMfYQfjBg76hnkmxHZxs2QhwkmI7X9ddJ2XK5PtZlSolDR9u/fO9XhmANI6dHCCZDh48qGeffVYvvPCCazKQLVs29e7dW507d1ZGDoSAyFCsmLRpU/DH2N/rrl2+ZgM4c6NG+VIBbffGH1T6a56qVZOmT5eyZ1fEscMeq7165x1p+3apUCHpnnt8O4AAIlpKYgOCHCAZPv/8c3Xs2FEbLAVG0g033KAXX3xRF154oddLA+Bnb2fpk5mgsHatb9cBZ8aarFiNU2KHEJYaaPVQI0Yoohw4IN16qzR5si84s909W6sFaS1aSO+9l3QrcQCeIV0NSCV//PGHrr/+erVo0cIFOBbUfPHFF5owYQIBDhBpbBfBam6SgxNnZ2fkyODtti14eOUVaf9+RZS77pKmTfN9boGNBWn+XagvvpDuv9/T5SENs+eiNU1JqJ4QZ4QgB0jAsWPHNHDgQDfz5ssvv1SGDBnUo0cPV49zneWfA4hMt90WvPjddnpq1ZIKFgznqmLPjBlJH4hZ44bFixUxfv7ZtuV9AVhCrG34Bx9I69eHe2VI68HNSy9JZctKRYpIdgK1cGGpd29fDSHOGEEOcIo5c+bosssuU8+ePXX48GHXZGDZsmUaMGCAskdifjmA/3Tp4tthSCxtzQ5kn3gi3KuKPXHnCKXG48LBApikur/Zc8ceB4QrwHnoIemRR6R16/67fedOqV8/qV49X4olIivImT17tpvwntBl0aJFofq2wBnbvn277r33XtWrV08rV650ndKsTfTXX3+t8uXLe708AMlhf6uWdmR1FZa+5i+Et6DHDmAthapJE8Uk26E4fjz5j7fH2s9q6FDp5ZelrVuT/29r1kw6YLAGD5UqKWL89VfwYbD+54k9DgiHzz6TXn3V9/mp9W12gmDJEunZZz1ZWiwIWZBjwxG3bNkS7/Lggw+qePHiqkIHE0SQkydPugGeZcuWdUGNBeKtW7fWmjVrdPfdd7vrAKJI48bSxo3SCy9IzZtLTZtKTz9tRXbSAw8o5kyc6BtYakGFtXK2QG/s2OABzyefSEWLWhcVqXt36eGHfdcffDB57Z/btw+ermYB5f/+J+XNq4jqvJfUzpIFivY4IBysMUdStW3jx9OS/QyFrbva8ePHdf7556tDhw566qmnkvVv6K6GUFuyZInatGmjBdZOVHJpamPHjtVVNh0dACKdBW92ptcOlPy1Jv4TM9dc46tBscDn1LPHN9+ccGc028m49lrfDk9SJ3h69ZIGDPD9m7jBg62lRAlp3jwpXz5FDAt8rd4h2GGP7U79+adUoEA4V4a0ymZxWe1aUpYvlypUCMeKIl5EdlezjlS7du3Sfffdl+hjjh496hYf9wKEgj23OnXq5HYVLcDJmTOnawm9cOFCAhwA0cHm0PhTWeIW09tBvF2mTpWefz7+v7Fg5NFHE/+adr/tDH39ddLf/7nnpHfflSpW/O82O+jo1EmaPz+yAhxjOzRWsxVMjx4EOAif5La8T+7j4M1OzrV2ZkjSV199lehj+vTpo759+552Ozs5SC32dP/4449dgGMplOa2227T0KFDVcS6mgBAMPaWGSkprJaKZ4FMsLSx/Pl9OxP+gcWzZkkNGgT/urab0bKlb2ZMcn8mNlTTUmqsK9SpO0eRxII42/0aPNiXzuffAcucWerZU7JMk0j5/SL2Jedv2IJuG3DM0PHQ7+RYG93EGgr4L6tXr473bzZt2qSpU6fqgSRyoa2blS3af9loW8tAKlm/fr2aNGnighoLcEqWLKlp06bpgw8+IMABkLjvvpNuucXXzMACANu5GDfOes0n/2vYgbQdzFitzDvvpE5xu+22JNXGeceO+C2RrS4pKfY1f/01+euwoMBaclsqWCQHOP4z4ta1yk5yWa1Dnz6+pgt23YIfAhyEk+2qBvsbtudrhw4EOGcoidYop+vatatatWoV9DElLBc3jtdff1158+Z1QxWDyZw5s7sAqenIkSNu5o1dLCXSnmMWUD/++OPKkiWL18sDEMksmGnb1nfG338wsnKl77YPP7T0BF/wE4zVxbRr59tRsYNo2/mwYKB1a2nIkDM/gEluIkbcepncuZN+vB1YnXeeYpr9/6x1L+ClRo18wfUzzyRcV2edIB9/3NMlRrOQp6vZl7/44ot10003aYi9mKcAjQdwtmynpl27dm4XxzRu3FijR492uzgAENSyZdaNJPFgwoIBq/Gw1KfEWAF/ixa+z0/9OnYgY2lhNpflTHYQrKPaN98kPtzS5Mrlaw3tP6FjwwULFUp69sZbb0l3353yNQFIuUmTfK3cZ8/2nZSwJgMdO0r33590q/Y0Zl8kNR6YNWuWfvvtN9c+GgiXzZs3u7S0a665xgU4hQsX1ocffqgpU6YQ4ABInpEjg7d3tYMR2+lJbCq53W+tlk1CgZLd9tFHvi5kZ8IOgoIFOLZ2GzIYd8d6xgwpR47E/40dUBUv7gu+AIRHs2bSzJm+OjG7WDc1a+tOgHNWQh7kvPrqq25mjs0gAULt77//dl3S7Pn20UcfKX369Hr00Uddnditt97KzBsAyTdtWtI1L7YjYgP7EmJnZa22NKmWxTag9EzYDpE/5erU1zbbZbr8cl8qjJ/VoNi/sSYBibnoIt/BFqm8QPjZ3y2BTaoJ+U/yveR2ZwHOkrWCtiGeS5cuddetFbTNvLHZNwCQYsF2SZLzuOQU71sQtW6dzogFNha4VK3qq+1Zs+a/jmpWM/TYY1L27L7brLDev6uU2EBMax5gZ5CpjQUQA2i8jai3e/duF9xUr17dBTi5c+fW+PHjNW/ePAIcAGeuWrWkz6pa04C4c2JOrYdJzpnbPHl0xizQsXTwVat8gYztHG3e7Osa5g9wzKuvJh7c+G3bJn377ZmvBQAiCEEOopY1tXjzzTdVpkwZF9TY9XvvvVdr1qzRww8/7FLVAOCM2c5HsHQ1q3m54w4pb96E77/mmqQ7r1ngceutOmsW7FhDgaJFEw7MLKUuqT5D9v9JLPUOAKIMiX+ISitXrlTbtm01Z84cd718+fIuNa1OnTpeLw2RzlKLpkyRFi70HdRZh6patZiPgdPVq+ebY/Hii/+1fvaz547NhQnWNdQ6/3TqJA0cmHCAYV+jWLHwFPnbjtOp/4dT2X1ezuOw7287Se+/75sjZD8bG1lhnaYAIIU41Y2ocvDgQTeQtnLlyi7AyZo1qwYMGKAlS5YQ4CBp33/v6xxlU6b795eefVay502lSmdeF4HYZUHBsGG+VK9Spf673dLArObFAmWrfwnGnmP33uv73L/D4t9ltoN463YWjiL/xo2TTlez+21uhxf27PGdcLC/RxvO+fHH0vDhvlRAa6NrHacAIJLm5JwN5uQgri+++EIdO3bUH/9O7LbhsiNGjNCFdjYVSMrPP1s3Cuno0dMP9uyMer58kjWtsJQf4FT2Vrlhg+/5Y8FJUmlop/rhB18XNQumrVbnllukm28OX5H/oUPSBRdYEWPCwY4FYBZgWGc1L362FuDYLk5CTRws2LTUwREjwr82AFEbGxDkIOJZUGPBjQU55oILLtDIkSNdkAMkm6UEffZZ4p2wLNCxydLPPRfulQHhsWCBb6fGAp5TJ6vbTpWl/3oR5NtA06R24i0I27TJ1wEOQJq1L5KGgQJn6vjx43r++eddvY0FOBkyZNDjjz/u6nEIcJAie/cGD3CM3WdpMkCssp3MFSukbt2kIkWkbNmk0qWlF16QFi3ybhfTanCS6mJnf5+ffhquFQGIATQeQESaO3eu2rRp4wIaY/U2Y8aMUQUKUHEmbPhhcmae7NjhS+WhMx9ilaXaWSMEu0SKXbuSrheynVZ7HAAkE+/kiCg7duxQq1atVLduXRfg5MuXz7WJnj17NgEOztx55yXvcVYrQYADhJe1vU7q785OUliABgDJxLs5IsLJkyf10ksvuZk3FtSkS5dOjzzyiJt5c88997jrwBmzOSbWXcrOBifG0mXuuSecqwJgrE10sHlExjrQWaMGAEgmghx4bunSpapZs6YLanbv3q1LL71U8+bN07hx43Recs/AA0l5+mnfx4QCZgt+7CDKZpoACK9LLvG12Q52MstacefMGc5VAYhyBDnwzP79+9W5c2ddccUVmj9/vnLmzKnhw4dr0aJFqlatmtfLQ6ypWdNXuGytf+1gynZu/MXOFkzbvJISJbxeJZA2WdMPmz1kJxz8f5/20f5ebeBqly5erxBAlKGFNMLOnnKffPKJOnXqpM2bN7vbbr31Vg0dOlTnn3++18tDrNu/X3rnHd8gRzugsvkcNrMkXPNKACRu61bfyQhrMmA1OPa3yQ4OgH8xJwcRa/369Wrfvr2mTp3qrpcsWVKjR49WY6uXAAAAABLBnBxEnCNHjqhv376qWLGiC3AyZcqk3r176+effybAAQAAQKoiyEHITZ8+XZUqVVKfPn109OhRNWrUSMuXL3fXs1ixN4DoHrT6/PPSxRdLmTL5Otl17Gjbtl6vDACQhpGuhpCxepsuXbroww8/dNcLFy7sGgu0bNmSltBALNiyRapdW/rtt/jDHK1oPGNGafJkqW5dL1cIAIghpKvBU3///bdGjBihsmXLugAnffr0evTRR7V69WrXYIAAB4gRd98t/fHH6dPqbebJ0aPS9df7Gj0AABBmBDlIVQsXLlTVqlVdUGMtou3zH374we3gsBsHxJBVq6SZMxMf4miBjwU4b78d7pUBAECQg9RhQzzbtGnj5tssWbJEuXPndsM8bajnZZdd5vXyAKQ2C3CSsytrj4sUhw9LU6ZIn3wiLVli/ey9XhEAIET+nYQHnBkr6Xr77bfVrVs37dixw912zz33aPDgwSpQoIDXywMQKraDY0FOsEDB7ktspyecbFfpueekF17wNUrwq1xZGjVKqlXLy9UBAEKAIAdnbOXKlWrbtq3mzJnjrpcrV05jxoxRvXr1vF4agFCrUuX0WpxTpU8vXXGFPNe6tfTKK6cHZD//LF19tTRjhlSnjlerAwCEAOlqSLFDhw6pZ8+eqly5sgtwsmbNqgEDBmjp0qUEOEBaUbOmndmQzjkn8cfYTs+DD8pTCxdKL7+c8I6TBWknTkiPPELqGgDEGIIcpMiXX36p8uXLa+DAga6L2nXXXed2dHr06OEGfAJIIyyAeecdyWZdnRro2A6OGTtWKlJEnho/3tfSOjEW6KxeLc2fH85VAQBCjCAHybJhwwa1aNFC119/vf744w9dcMEFmjBhgr744gtddNFFXi8PgBcuv1xatEi66ab4gU7VqtLEidJDD8lzK1cmry5ozZpwrAYAECbU5CCo48ePa9iwYerbt69LU8uQIYO6du2qp556StmzZ/d6eQC8ZilrH31kLRZtArCUK5dUtKgiRo4cSTdIMLyeAUBMIchBor755hvXFnrFihXueu3atTV27FhVqFDB66UBiDR58vgukcZ2mZJqY505s9SoUbhWBAAIA9LVcBprBX3fffepTp06LsDJly+f3njjDddkgAAHQFS56y4pb97EGyRY/ZA1HsidO9wrAwCEEEEOAk6ePKmXX35ZZcqUcUGNefjhh7VmzRrde++9SpecwX8AEEly5pSmT/cFMXFfw/zNCJo3lwYP9mx5OMXx475hrc2a+eYYNW4svf22dOSI1ysDEGXS/WPTHCPUvn37lCtXLu3du1fnnnuu18uJacuWLXOpad9//727bu2hx40bp2rVqnm9NACwNwRfN7cFC3y7L3XrSrfdJmXNmrx/bzVDb77pqx+yr1W6tG8Hx9LU/N3g4C37HTVp4mv7bTtv1t7bfjfWAa9MGWnWLO+79QGImtiAICeN279/v3r37q0RI0boxIkTypEjh5599lm1b9/eNRkAAM99/rn0v/9Jhw//F5DYAfB550kTJljBoNcrRGpo2tS362a/21PZ+9Ell0g//hh/Rw5AmrIvBbEBp6/SKIttP/74Y5UtW9Z1T7MAp2XLllq9erU6depEgAMgMtju8s03+wIcOydnB8D+g+A9e6RrrpHWrvV6lThby5dLU6YkHOAYawO+ZIk0e3a4VwYgShHkpEG//PKLmjZtqltvvVWbN2/WxRdfrClTpuijjz7S+eef7/XyAOA//fr5PiaUdGBpTFbDMXRo2JeFEOzWJdYcws9Ovn32WbhWBCDKEeSkIUePHtUzzzzjOqRNnTpVmTJl0tNPP62ff/5Z19jZUACIJLZTM3ly4mf3/Wf4rTA9cjOvkRwHDyavNsoeBwDJQE5SGjFjxgy1bdtW69atc9cbNmyo0aNHq7QV3wJApAY5yQleDh3y7ehkyhSOVSEU7L3IfofB2M4d71kAkomdnBi3ZcsW3XHHHWrUqJELcAoXLqwPPvhA06ZNI8ABENny5fuv1XMwNoSUACe6tWwp5cgR/DG203PvveFaEYAoR5ATo6yRwMiRI11jAQtq0qdPr44dO2rVqlW67bbbmHkDIPLZQe+ttwYPdKyO46GHwrkqhEL27NKIEcEf89xzUqFC4VoRgChHkBODFi5cqKpVq7qgxlrt2eeLFi3Siy++6NruAUDUeOopKXPmhIvS7ba8eaVOnbxYGVLbffdJ778vFSsW//YCBaRx46Tu3b1aGYAoxJycGLJ792716tVL48ePdy2ic+fOrQEDBuihhx7SOUl1rQGASPXDD74dnd9+8+3q+FtJ29yUTz+VSpXyeoVITVZ788030ubNUv78vsGvGTN6vSoAURYb0HggBlhA884776hbt27avn27u+3uu+/W4MGDVbBgQa+XBwBnp0oVaf16aeZMaf583w6OHfjWqMFgyFhktTf2+wWAs0CQE+Wsxsa6ps3+d0Ca1eCMHTtW9erV83ppAJC6B76NGvkuAAAkgZqcKHXo0CGXmla5cmUX4GTNmlX9+/fXsmXLCHAAAACQphHkRKGJEyeqfPnyrt7m+PHjat68uVasWKGePXu6AZ8AotSMGdJ110k5c/o6izVo4JsEH7mlkwAARCSCnCiyYcMG3Xjjjbruuuv0xx9/qFixYvrss8/0xRdfqHjx4l4vD8DZePppXyrW5MnSgQO+ye5z5kgtWkht2hDoAACQAgQ5UcB2awYNGqRy5cppwoQJypAhg7p37+7qcVq0aMHMGyDaffml9Oyzvs+ta5if//Px46XXX/dmbQAARCGCnAj3zTff6LLLLtPjjz/u6nBq166tJUuW6Pnnn1d2G54GIPq98ELCc2D87ESGPYbdHAAAkoUgJ0Lt2LFD9913n+rUqePqbfLly6fXX39dc+bMUcWKFb1eHoDUYrs1c+fG38E5lQU3K1faC0M4VwYAQNQiyIkwJ0+e1Msvv6wyZcrojTfecLfZMM/Vq1erVatWpKYBscaCm+Tu0Bw/HurVAAAQEwhyIoi1f65Vq5Yefvhh7d6927WHnjdvnl566SXlzZvX6+UBCAXriFimTNJDLQsUkBjui2hnM91uuMH3vLfZR1mzShdcIHXrJv36q9erAxBDCHIiwP79+9WlSxddccUV+v7775UjRw4NHTpUP/zwg6pXr+718gCEWseOwe+3g8G2baUMzG9GFBs6VKpf3+Yg+HYlbQfzyBFp40bffaVLS+++6/UqAcSIdP/8E7mVrPv27VOuXLm0d+9enXvuuYo19qP/9NNP1alTJ/3555/utltuuUXDhg1T0aJFvV4egHCxA75mzaSZMy1nNf591pDg8sulr7+WaDaCaPX991KNGkk/zgL6+fOlK68Mx6oARJmUxAbs5Hjkl19+0bXXXquWLVu6AKdEiRKaPHmyPv74YwIcIK3JmNF3dttm5eTL99/tuXJJXbsS4CD6jRiRvJ1IC3KGDQvHigDEOHZywuzo0aNu5k3//v115MgRZcqUybWH7tmzp7JabjKAtM12ddau9e3olColZcni9YqAs2d1pX/9lbzH2nP+8OFQrwhAFEpJbECCdxjNnDlTbdu21Vo7gJHUoEEDjRkzRqUtDxkA/Ls6FSp4vQogdZ2ahhnM0aO+x9uuDgCcIV5BwmDr1q363//+p4YNG7oAp1ChQnr//fc1ffp0AhwAQGz76ScpR47kP754cQIcAGeNV5EQOnHihEaNGuVm3lhQkz59enXo0MHNvLn99tuZeQMAiG0DBkiVK0ubNyfv8fa+aJ0EAeAska4WItb+uXXr1vrxxx/d9SpVqmjcuHGuTTQAADHvk0+kXr2Sn65mnQQvuURq3TrkSwMQ+9jJSWV79uxRu3btVLVqVRfgWHGU1d3Mnz+fAAcAkHb075/8tDPrvHbnnb5hoXQSBJAK2MlJJdak7t1331XXrl21fft2d9tdd92lIUOGqCBTygEAacmWLdKSJckLbj7+WKpZU8qfPxwrA5BGEOSkAquxsa5pX9ssC0lly5Z1uzf1bbIzAABpzaFDyXvc339L119PowEAqY5XlbNw6NAhPfHEE6pUqZILcLJkyaLnnntOy5YtI8ABAKRdRYpIyZn9VqIEAQ6AkAjZK4u1Sr7hhhuUL18+N6ynVq1agZ2OWDBp0iRVqFDBDfU8fvy4mjVrppUrV6pXr15uwCcAAGmWBTitWvmaCSTGgps2bcK5KgBpSMiCnObNm+vvv//WrFmzXAF+5cqV3W02Myaabdy4UTfddJP7v/z+++8qWrSoPvvsM3355Zcqbr39AQCA9PTTUqFCvrqbxDqpEeQAiKYgZ+fOnVq3bp169OjhUrlKlSqlgQMHuvSu5cuXKxrZbo01EShXrpwLajJkyKDHHntMq1atUosWLZh5AwBAXBbgzJ8vNW3qm3/jZ0HPXXdJc+bQSQ1AdDUeyJs3rxuA+dZbb+nyyy9X5syZNX78eBUoUCBoG+WjR4+6i9++ffsUKSwdbfr06e5zS70bO3asKlas6PWyAACIXEWLSl98IW3YINncONvBqV6dTmoAojPIsV2NGTNmuB2OnDlzKn369C7AmTJlivLkyZPovxswYID69u2rSHTfffdp8eLFGjx4sO699173fwIAAMlwwQW+CwCESbp/bMBLMln62fPPPx/0MZa+Zbs4FuBYipd1H8uaNateeeUVffHFF1q0aJEKFy6c7J2cYsWKae/eva55gZfsx2TryJ07t6frAAAAANKiffv2KVeuXMmKDVIU5OzYsUO7du0K+pgSJUrom2++UePGjbV79+54C7DanAceeMAFS6n9HwEAAAAQu1ISG6QoXS1//vzukhRrMGBOTemy6ydPnkzJtwQAAACAFAlJYUn16tVd7Y3VrthgTJuZY53IfvvtN1fADwAAAABRFeTYAFBrMnDgwAFdffXVqlKlir799lt9/vnnbl4OAAAAAIRKimpywo2aHAAAAAApjQ3ogwwAAAAgphDkAAAAAIgpBDkAAAAAYgpBDgAAAICYQpADAAAAIKYQ5AAAAACIKQQ5AAAAAGIKQQ4AAACAmEKQAwAAACCmEOQAAAAAiCkEOQAAAABiCkEOAAAAgJhCkAMAAAAgphDkAAAAAIgpBDkAAAAAYgpBDgAAAICYQpADAAAAIKYQ5AAAAACIKQQ5AAAAAGIKQQ4AAACAmEKQAwAAACCmEOQAAAAAiCkEOQAAAABiCkEOAAAAgJhCkAMAAAAgphDkAAAAAIgpBDkAAAAAYgpBDgAAAICYQpADAAAAIKYQ5AAAAACIKQQ5AAAAAGIKQQ4AAACAmEKQAwAAACCmEOQAAAAAiCkEOQAAAABiCkEOAAAAgJhCkAMAAAAgphDkAAAAAIgpBDkAAAAAYgpBDgAAAICYQpADAAAAIKYQ5AAAAACIKQQ5AAAAAGIKQQ4AAACAmEKQAwAAACCmEOQAABBpTp6UvvpKuvlmqXJlqX596eWXpYMHvV4ZAESFDF4vAAAAxHHokHTjjdK0adI550gnTkjp0kmzZ0vPPivNmiWVLOn1KgEgorGTAwBAJGnbVpoxw/e5BTjmn398H7dskRo1ko4d8259ABAFCHIAAIgUmzdLb7/tS1dLyN9/S7//Lk2YEO6VAUBUIcgBACBSTJyYeIDjZyls//d/4VoRAEQlghwAACKFNRawICYYS2GjAQEABEWQAwBApChV6r86nMRkyCCVLh2uFQFAVCLIAQAgUjRpIhUq5Oumlhiry3nwwXCuCgCiDkEOAACRwnZpxo/3fZ5YoNOli1SuXFiXBQDRhiAHAIBIcv31vgYEF18c//ZcuaSBA6UhQ7xaGQBEDYaBAgAQaa69VmraVPr+e+mPP6TcuaX69aUsWbxeGQBEBYIcAAAikaWr1ajhuwAAUoR0NQAAAAAxhSAHAAAAQEwhyAEAAAAQUwhyAAAAAMSUkAU5ixcvVqNGjZQ7d27lzZtXDz/8sA4cOBCqbwcAAAAAoQtyNm/erIYNG6pkyZJasGCBpkyZohUrVqhVq1ah+HYAAAAAENoW0hMnTlTGjBk1evRopU/vi6PGjRunSpUqaf369S74AQAAAICo2ck5evSoMmXKFAhwTNasWd3Hb7/9Nui/27dvX7wLAAAAAHge5Fx99dXaunWrBg8erGPHjmn37t3q0aOHu2/Lli2J/rsBAwYoV65cgUuxYsVCsTwAAAAAMSxFQY4FKunSpQt6Wb16tSpUqKA333xTL7zwgrJly6ZChQqpePHiKliwYLzdnVP17NlTe/fuDVw2btyYGv9HAAAAAGlIun/++eef5D54x44d2rVrV9DHlChRwqWq+W3btk3Zs2d3AdC5556rDz74QC1btkzW97N0NdvRsYDH/i0AAACAtGlfCmKDFDUeyJ8/v7ukhO3emNdee01ZsmRxbaUBAAAAIKq6q5lRo0apRo0aypEjh6ZPn67HHntMAwcOdHNzAAAAACDqgpyFCxeqd+/ebgBo2bJlNX78eN19992h+nYAAAAAENog56233grVlwYAAACA8LaQBgAAAACvEOQAAAAAiCkEOQAAAABiCkEOAAAAgJhCkAMAAAAgpoSsu1pq+OeffwLTTQEAAACkXfv+jQn8MULUBjn79+93H4sVK+b1UgAAAABESIyQK1euoI9J909yQiGPnDx5Ups3b1bOnDmVLl06zyNHC7Y2btyoc88919O1IPXwe409/E5jE7/X2MPvNDbxe409+yLod2phiwU4RYoUUfr06aN3J8cWX7RoUUUS++V6/QtG6uP3Gnv4ncYmfq+xh99pbOL3GnvOjZDfaVI7OH40HgAAAAAQUwhyAAAAAMQUgpxkypw5s3r37u0+Inbwe409/E5jE7/X2MPvNDbxe409maP0dxrRjQcAAAAAIKXYyQEAAAAQUwhyAAAAAMQUghwAAAAAMYUgBwAAAEBMIcgBAAAAEFMIcpJp9OjRuuiii5QlSxZdddVVWrhwoddLwlmYO3eurrvuOhUpUkTp0qXThAkTvF4SztKAAQN05ZVXKmfOnCpQoIBatGihNWvWeL0snIWxY8eqUqVKgSnb1atX1+TJk71eFlLRwIED3Wtwp06dvF4KzkKfPn3c7zHupWzZsl4vC6ngzz//1F133aW8efMqa9asuuSSS/TDDz8oGhDkJMOHH36oLl26uB7hixcvVuXKlXXNNddo+/btXi8NZ+jgwYPu92jBK2LDnDlz1K5dO82fP1/Tp0/X8ePH1bhxY/e7RnQqWrSoOwj+8ccf3Zvq1VdfrRtuuEErVqzwemlIBYsWLdL48eNdIIvoV6FCBW3ZsiVw+fbbb71eEs7S7t27VbNmTWXMmNGdYFq5cqVeeOEF5cmTR9GAOTnJYDs3doZ41KhR7vrJkydVrFgxdejQQT169PB6eThLdsbps88+c2f+ETt27NjhdnQs+KlTp47Xy0EqOe+88zR48GA98MADXi8FZ+HAgQO6/PLLNWbMGPXr10+XXnqphg8f7vWycBY7OZYRsXTpUq+XglTUo0cPfffdd/rmm28UjdjJScKxY8fcWcSGDRsGbkufPr27/v3333u6NgCJ27t3b+CgGNHvxIkT+uCDD9zOnKWtIbrZrmuzZs3ivbciuq1bt86lgJcoUUJ33nmnNmzY4PWScJa++OILValSRS1btnQnDS+77DK9/PLLihYEOUnYuXOne3MtWLBgvNvt+tatWz1bF4DE2W6r5fjbNnvFihW9Xg7Ows8//6wcOXIoc+bMat26tdt1LV++vNfLwlmwYNVSv62ODrGT8fLGG29oypQprpbut99+U+3atbV//36vl4az8Ouvv7rfZ6lSpTR16lS1adNGHTt21JtvvqlokMHrBQBAKM4SL1++nJzwGFCmTBmXAmM7c5988onuvfdel4JIoBOdNm7cqEcffdTVzVkjH8SGpk2bBj63GisLei688EJ99NFHpJZG+QnDKlWqqH///u667eTYe+u4cePca3GkYycnCfny5dM555yjbdu2xbvdrhcqVMizdQFIWPv27TVx4kR9/fXXrnAd0S1TpkwqWbKkrrjiCnfm3xqGvPjii14vC2fI0r+taY/V42TIkMFdLGgdMWKE+9wyJxD9cufOrdKlS2v9+vVeLwVnoXDhwqedUCpXrlzUpCIS5CTjDdbeXGfOnBkvsrXr5IUDkcN6qFiAY+lMs2bNUvHixb1eEkLAXn+PHj3q9TJwhho0aOBSEG13zn+xM8VWw2Gf20lFxEZjiV9++cUdJCN61axZ87RRDGvXrnW7dNGAdLVksPbRti1nL8RVq1Z1HWCs+PW+++7zemk4ixfguGeYLH/Y3mCtSP2CCy7wdG048xS19957T59//rmbleOvmcuVK5fr7Y/o07NnT5cGY3+Tlttvv9/Zs2e73HBEJ/vbPLVOLnv27G4GB/Vz0atbt25u9pwd/G7evNmN3LCA9Y477vB6aTgLnTt3Vo0aNVy62q233upmRL700kvuEg0IcpLhtttuc+1on376aXfgZK0urbju1GYEiB42c6N+/frxAlljwawVTyL6WHGkqVevXrzbX3/9dbVq1cqjVeFsWFrTPffc42ZuWLBquf4W4DRq1MjrpQGIY9OmTS6g2bVrl/Lnz69atWq5mWX2OaLXlVde6bIj7ITTM8884zIk7ES/7bxGA+bkAAAAAIgp1OQAAAAAiCkEOQAAAABiCkEOAAAAgJhCkAMAAAAgphDkAAAAAIgpBDkAAAAAYgpBDgAAAICYQpADAAAAIKYQ5AAAAACIKQQ5AAAAAGIKQQ4AAAAAxZL/B1huhGnRvjNXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")\n",
    "\n",
    "# 绘制 3 条不同的分割线\n",
    "x_temp = np.linspace(0, 6)\n",
    "for m, b in [(1, -8), (0.5, -6.5), (-0.2, -4.25)]:\n",
    "    y_temp = m * x_temp + b\n",
    "    plt.plot(x_temp, y_temp, \"-k\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afeea3d",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "source": [
    "**------以下是代码解释------**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a808b47b",
   "metadata": {},
   "source": [
    "`plt.plot(x_temp, y_temp, \"-k\")` 这行代码是用matplotlib绘制直线的命令，让我逐个解释其参数：\n",
    "\n",
    "1. `x_temp`: x轴坐标数据，这里是使用`np.linspace(0, 6)`生成的等间距数组\n",
    "2. `y_temp`: y轴坐标数据，这里通过`y_temp = m * x_temp + b`计算得到\n",
    "3. `\"-k\"`: 是格式字符串，用于指定线条的样式：\n",
    "   - `-` 表示实线\n",
    "   - `k` 表示黑色(black)的意思\n",
    "\n",
    "所以这行代码的整体含义是：绘制一条黑色的实线，其中：\n",
    "- 横坐标由x_temp指定\n",
    "- 纵坐标由y_temp指定\n",
    "- 线型为实线\n",
    "- 颜色为黑色\n",
    "\n",
    "这在代码中是用来绘制SVM的分割线，通过循环绘制了3条不同的分割线，每条线都有不同的斜率(m)和截距(b)。\n",
    "\n",
    "matplotlib中常见的颜色代码：\n",
    "- 'k': 黑色(black)\n",
    "- 'r': 红色(red)\n",
    "- 'g': 绿色(green)\n",
    "- 'b': 蓝色(blue)\n",
    "- 'y': 黄色(yellow)\n",
    "- 'w': 白色(white)\n",
    "\n",
    "常见的线型：\n",
    "- '-': 实线\n",
    "- '--': 虚线\n",
    "- ':': 点线\n",
    "- '-.': 点划线"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9283ef",
   "metadata": {},
   "source": [
    "**------以上是代码解释------**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97e1d17",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "然后，可以使用 ` fill_between  ` 方法手动绘制出分类间隔。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "26f05075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")\n",
    "\n",
    "# 绘制 3 条不同的分割线\n",
    "x_temp = np.linspace(0, 6)\n",
    "for m, b, d in [(1, -8, 0.2), (0.5, -6.5, 0.55), (-0.2, -4.25, 0.75)]:\n",
    "    y_temp = m * x_temp + b\n",
    "    plt.plot(x_temp, y_temp, \"-k\")\n",
    "    plt.fill_between(x_temp, y_temp - d, y_temp + d, color=\"#f3e17d\", alpha=0.5)"
   ]
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   "source": [
    "\n",
    "上图为了呈现出分类间隔的效果，手动指定了参数。 \n",
    "\n",
    "从上面的绘图中可以看出，不同的分割线所对应的间隔大小是不一致的，而支持向量机的目标是找到最大的分类间隔所对应的分割线。在这里这个间隔指的是分类点与超平面之间的几何间隔，与此同时如果需要了解支持向量机的求解原理就要引入另一个与之关联的概念：函数间隔。 \n",
    "\n",
    "Note \n",
    "\n",
    "以下支持向量机推导过程比较复杂，可根据自身数学基础尽量掌握，不做强制要求。 \n",
    "\n",
    "##  17.6.  函数间隔  # \n",
    "\n",
    "一个样本点在支持向量机中是如何进行分类判断的呢？当超平面  $w^{T}x+b=0$  确定时，样本点到超平面的距离可以用  $|w^{T}x+b|$  来表示，而通过观察  $w^{T}x+b$  的符号与样本点的类别  $y$  符号是否一致来判断分类正确性。 \n",
    "\n",
    "这个过程，可以用公式  $(y-\\frac{(m+n)}{2})(w^{T}x +b)$  的正负性来表示。其中，  $m$  、  $n$  分别为两个样本点类别值，  $y$  为  $m$  ，  $n$  中任意一个，对于任意点都能用此公式进行分类判断。于是，这里我们便引入了函数间隔的概念。 \n",
    "\n",
    "首先我们定义函数间隔公式，为简化公式这里定义类别  $m=1$  ，  $n=-1$  ： \n",
    "\n",
    "$$ h_{1} = y(w^{T}x +b) = yf(x) \\tag{1} $$ \n",
    "\n",
    "集合  $T$  中每个样本点  $(x_i, y_i)$  关于超平面  $(w, b)$  都有一个函数间隔，其中最小值便是超平面  $(w, b)$  关于集合  $T$  的函数间隔，定义如下： \n",
    "\n",
    "$$ h_{1} = min\\: h_{1i}\\: (i=1,2,...,n) \\tag{2} $$ \n",
    "\n",
    "所有点到超平面的函数间隔都  $\\geq h_{1}$  ，有了最小函数间隔  $h_{1}$  ，那么现在是否可以直接用来确定超平面？当然不行，这里就要提到函数间隔的局限性。 \n",
    "\n",
    "其实到这里你也应该可以看出我们这样定义函数间隔公式的一个问题，当按比例的改变  $w$  与  $b$  的值，  $f(x)$  也会成比例的改变大小，但是超平面  $(w, b)$  却不会改变。支持向量机的机制就是找到最大分类间隔，如果间隔可以任意改变，但超平面却不随之改变，那么这个度量就是没有意义的。 \n",
    "\n",
    "因此仅有函数间隔是不够的，要是能有一个方法对法向量  $w$  进行约束就好了，恰好前面提到过的几何间隔能帮我们解决这个问题。 \n",
    "\n",
    "##  17.7.  几何间隔  # \n",
    "\n",
    "我们定义一个点  $x$  ，其到超平面上的垂直映射点为  $x_0$  ，两点之间的距离为  $h$  ，  $w$  表示超平面的法向量。如下图所示： \n",
    "\n",
    "![image](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid7506timestamp1546494481697.png)\n",
    "\n",
    "根据平面几何知识，有： \n",
    "\n",
    "$$ x = x_{0}+h\\frac{w}{\\left \\| w \\right \\|} \\tag{3} $$ \n",
    "\n",
    "其中，  $\\frac{w}{\\left \\| w \\right \\|}$  不难理解，一个向量除以它的模表示的就是单位向量。此时，对公式两边同时乘以一个  $w$  得到： \n",
    "\n",
    "$$ w ^ { T } x = w ^ { T } x _ { 0 } + h \\frac { \\| w \\| ^ { 2 } } { \\| w \\| } \\tag{4} $$ \n",
    "\n",
    "此外，从图中可以看到  $x_0$  为超平面上的点，满足  $f(x_0)=0$  ，所以带入超平面有  $w^{T}x_0 + b = 0$  。最后，带入上面的公式求解距离  $h$  得到如下结果： \n",
    "\n",
    "$$ h = \\frac{w^{T}x+b}{\\left \\| w \\right \\|}=\\frac{f(x)}{\\left \\| w \\right \\|} \\tag{5} $$ \n",
    "\n",
    "上面的公式很好理解，我们都知道在平面中点到直线的距离公式为  $D = \\frac{Ax_{0}+By_{0}+C}{\\sqrt{A^{2}+B^{2}}}$  ，当升到更高维度时就与上面一致了。此处为了取得距离  $h$  的绝对值，需要乘上类别  $y$  ，得到如下结果： \n",
    "\n",
    "$$ \\left | h \\right | = yh= \\frac{h_{1}}{\\left \\| w \\right \\|} \\tag{6} $$ \n",
    "\n",
    "从式中可以看到几何间隔中对法向量  $w$  做了约束，  $h_{1}$  也就是上面我们得出的函数间隔，只是人为定义的一个距离，而几何间隔  $\\left | h \\right |$  才是直观上点到超平面的距离，它的取值不会因  $w$  和  $b$  缩放而改变，只会随超平面的改变而改变。 \n",
    "\n",
    "通过上面介绍，可以看出函数间隔除以  $\\left \\| w \\right \\|$  得到的就是几何间隔，而函数间隔的本质其实可以理解为  $\\left | f(x) \\right |$  。 \n",
    "\n",
    "##  17.8.  拉格朗日对偶性  # \n",
    "\n",
    "在对函数间隔与几何间隔有了一定的了解后，现在我们面临另一个问题，如何求解这个分类间隔最优化问题。 \n",
    "\n",
    "前面提到因为函数间隔  $h_{1}$  的缩放问题，不适合作为最大化间隔值，从而引入了几何间隔  $h$  来解决这个问题。所以这里我们可以定义最大化分类间隔的目标函数如下： \n",
    "\n",
    "$$ max\\: \\left | h \\right |= max\\: \\frac{h_{1}}{\\left \\| w \\right \\|} \\tag{7} $$ \n",
    "\n",
    "由于每个点的函数间隔都是大于或等于最小函数间隔，所以同时需要满足以下约束条件： \n",
    "\n",
    "$$ y_{i}(w^{T}x_{i}+b)\\geq h_{1}\\: \\: (i=1,2,...,n) \\tag{8} $$ \n",
    "\n",
    "我们令函数间隔  $h_{1}$  为 1，得出下面的二次规划问题： \n",
    "\n",
    "$$ max\\: \\frac{1}{\\left \\| w \\right \\|} \\: \\: \\: \\: s.t.\\: \\: y_{i}(w^{T}x_{i}+b)-1\\geq 0\\: \\: (i=1,2,...,n) \\tag{9} $$ \n",
    "\n",
    "二次规划问题中前面为目标函数，  $s.t.$  后面为约束条件。 \n",
    "\n",
    "同时便于后面运算，等价转换目标函数为  $min \\frac{1}{2}\\left \\| w \\right \\|^{2}$  ，得到下面的凸二次规划问题： \n",
    "\n",
    "$$ min\\: \\frac{1}{2}\\left \\| w \\right \\|^{2} \\: \\: \\: \\: s.t.\\: \\: y_{i}(w^{T}x_{i}+b)-1\\geq 0\\: \\: (i=1,2,...,n) \\tag{10} $$ \n",
    "\n",
    "故上式表达为：在约束条件  $y_{i}(w^{T}x_{i}+b)-1\\geq 0\\: \\: (i=1,2,...,n)$  的情况下，最大化几何间隔  $\\frac{1}{\\left \\| w \\right \\|}$  。 \n",
    "\n",
    "为了公式推导方便，我们直接「令函数间隔  $h_{1}$  为 1」，来得到二次规划问题的表达式。那么，这样做会不会影响了后续运算的结果呢？ \n",
    "\n",
    "首先，简单点可以通过函数间隔的性质来理解，前面提到过函数间隔是可以任意伸缩的；其次，也可以通过换元方法来理解，将  $w$  换为  $\\frac{w}{h_{1}}$  ，  $b$  换为  $\\frac{b}{h_{1}}$  ，  $h_{1}$  自然就是 1。同样，需要将目标函数替换。 \n",
    "\n",
    "目前我们已经得到了支持向量机凸二次规划问题的表达式，接下来就可以求解了。对于这样特殊形式的结构，通常的做法是使用拉格朗日对偶性将原始问题转变为其对偶问题，即通过求解与原始问题等价的对偶问题得到原始问题的最优解。通过这种方法会使得原始问题更容易求解，以及后续更自然的引入核函数求解非线性问题。 \n",
    "\n",
    "在满足 [ 条件 ](http://www.baike.com/wiki/%E6%8B%89%E6%A0%BC%E6%9C%97%E6%97%A5%E4%B9%98%E5%AD%90%E6%B3%95) 的情况下，拉格朗日对偶性通过给每一个约束条件加上一个拉格朗日乘子  $\\lambda $  ，将约束条件融合到目标函数中，仅通过一个函数表达出问题。所以上面的凸二次规划问题转换如下： \n",
    "\n",
    "$$ L(\\lambda ,w,b)=\\frac{1}{2}\\left \\| w \\right \\|^{2}-\\sum_{i=1}^{n}\\lambda_{i}(y_{i}(w^{T}x_{i}+b)-1) \\tag{11} $$ \n",
    "\n",
    "现在问题就得到了简化，我们只需要最大化  $L$  即可： \n",
    "\n",
    "$$ \\theta (w)=max\\: L(\\lambda ,w,b)\\: \\: \\: \\: \\lambda_{i}\\geq 0 \\tag{12} $$ \n",
    "\n",
    "上面的融合目标函数是如何等价解决我们问题的呢？ \n",
    "\n",
    "当某个约束条件不满足，也就是函数间隔小于 1 时 只要  $\\lambda_{i}$  定义足够大，  $L$  就趋近无穷。而当所有约束条件都满足时，则最优值表示为  $\\frac{1}{2}\\left \\| w \\right \\|^{2}$  ，也就是最初我们需要最小化的量。因为当  $x_{i}$  为支持向量时  $(y_{i}(w^{T}x_{i}+b)-1)=0$  ，此时最优值为  $\\frac{1}{2}\\left \\| w \\right \\|^{2}$  ；当  $x_{i}$  为非支持向量时，函数间隔必定大于 1，且  $\\lambda_{i}$  取值非负，为了满足最大化，  $\\lambda_{i}$  必须等于 0，此时的最优值依旧是  $\\frac{1}{2}\\left \\| w \\right \\|^{2}$  。 \n",
    "\n",
    "其实，通过上面的解释还能发现一些很有趣的结论，就是在满足约束条件时非支持向量的  $\\lambda$  都等于 0，后面整体为 0；而支持向量的  $\\lambda$  虽然有无数个取值，但是由于函数间隔等于 1，因此后面整体取值依旧为 0 。 \n",
    "\n",
    "通过一系列的等价转换后，现在我们可以归纳目标函数，如下： \n",
    "\n",
    "$$ min_{(w,b)}\\, max_{(\\lambda \\geq 0)}\\, L(\\lambda ,w,b) = p^{*} \\tag{13} $$ \n",
    "\n",
    "若此时直接对目标函数求解并不是那么容易的，因为此时  $\\lambda$  为不等式的约束，我们还的面对  $w$  ，  $b$  两个参数，所以通常的做法是转换为对偶问题，将最大最小优化的位置进行交换： \n",
    "\n",
    "$$ max_{(\\lambda \\geq 0)}\\, min_{(w,b)}\\, L(\\lambda ,w,b) = d^{*} \\tag{14} $$ \n",
    "\n",
    "当然公式并不能随意交换位置，需要满足一定条件。当原始问题与对偶问题都有最优解时，可以得到： \n",
    "\n",
    "$$ d^{*}=max_{(\\lambda \\geq 0)}\\, min_{(w,b)}\\, L(\\lambda ,w,b)\\leq min_{(w,b)}\\, max_{(\\lambda \\geq 0)}\\, L(\\lambda ,w,b) = p^{*} \\tag{15} $$ \n",
    "\n",
    "对于所有的优化问题如上公式都成立，即使原始问题非凸，这个性质叫做弱对偶性，当然与之对应的就有强对偶性，强对偶需满足： \n",
    "\n",
    "$$ d^{*}=p^{*} \\tag{16} $$ \n",
    "\n",
    "在支持向量机中满足 [ Slater条件 ](https://www.cnblogs.com/dreamvibe/p/4349886.html) 与 [ KKT条件 ](https://zhuanlan.zhihu.com/p/38163970) 后，我们直接假定强对偶性成立，通过求解对偶问题来得到原始问题的解。在验证满足条件之后我们就可以对第二个公式的最优值  $d^{*}$  进行求解了。 \n",
    "\n",
    "##  17.9.  对偶问题求解  # \n",
    "\n",
    "关于  $d^{*}$  的求解我们可以分为两个部分展开，但是由于需要有较强的逻辑性以及对数学知识有较强的功底，因此这部分不做要求。其实了解了上面介绍的三个内容后你应该对支持向量机数学原理有了不错的掌握。 \n",
    "\n",
    "在固定  $\\lambda $  的前提下，对  $w$  和  $b$  进行求偏导处理，使得  $L$  关于  $w$  ，  $b$  最小化，并将导数设为 0，可以得到： \n",
    "\n",
    "$$ \\bigtriangledown _{w}L(\\lambda ,w,b)=w-\\sum_{i=1}^{n}\\lambda_{i}y_{i}x_{i}=0 \\tag{17} $$ \n",
    "\n",
    "$$ \\bigtriangledown _{b}L(\\lambda ,w,b) = \\sum_{i=1}^{n}\\lambda_{i}y_{i}=0 \\tag{18} $$ \n",
    "\n",
    "现在将两个式子代入到上面的  $L$  中就可以得到最终的公式： \n",
    "\n",
    "$$ L(\\lambda ,w,b)=\\sum_{i=1}^{n}\\lambda_{i}-\\frac{1}{2}\\sum_{i,j=1}^{n}\\lambda_{i}\\lambda_{j}y_{i}y_{j}x_{i}^{T}x_{j} \\tag{19} $$ \n",
    "\n",
    "所以现在我们的问题就变成了该对偶形式： \n",
    "\n",
    "$$ max_{(\\lambda )}\\: \\sum_{i=1}^{n}\\lambda_{i}-\\frac{1}{2}\\sum_{i,j=1}^{n}\\lambda_{i}\\lambda_{j}y_{i}y_{j}x_{i}^{T}x_{j} \\tag{20} $$ \n",
    "\n",
    "$$ s.t.\\: \\:\\sum_{i=1}^{n}\\lambda_{i}y_{i}\\: \\: \\: \\: \\lambda_{i}\\geq 0,\\: \\: i=1,2,...,n $$ \n",
    "\n",
    "目前，问题中只有  $\\lambda$  一个变量，接下来就可以使用序列最小优化算法针对这个拉格朗日乘子进行求解了。 \n",
    "\n",
    "序列最小优化算法（Sequential minimal optimization，SMO）由微软研究院的约翰·普莱特于 1998 年发明，目前被广泛使用于 SVM 的训练过程中。先前可用的 SVM 训练方法必须使用复杂的方法计算量大，并需要昂贵的第三方二次规划工具。而 SMO 算法则较好地避免了这一问题。 \n",
    "\n",
    "简单理解它的思路就是通过一次迭代只优化两个变量而固定剩余的变量。从而将一个大的优化问题分解为若干个小的优化问题，而这些小的优化问题往往更容易求解。 \n",
    "\n",
    "虽然序列最小化算法在支持向量机求解中发挥了强大的作用，但同时带来的就是相对于第一步更为复杂的数学推导。于此，实验中将不再对其进行大篇幅的描述，有一定数学功底和想深入的同学可以自行查阅相关资料或参考本实验给出的链接学习。 \n",
    "\n",
    "  * [ 序列最小优化算法 ](https://zh.wikipedia.org/wiki/%E5%BA%8F%E5%88%97%E6%9C%80%E5%B0%8F%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95)\n",
    "\n",
    "  * [ 序列最小优化算法（SMO）浅析 ](https://www.jianshu.com/p/eef51f939ace)\n",
    "\n",
    "  * [ 机器学习算法实践 SVM 中的 SMO 算法 ](https://zhuanlan.zhihu.com/p/29212107)\n",
    "\n",
    "##  17.10.  线性支持向量机分类实现  # \n",
    "\n",
    "上面，我们对支持向量机中函数间隔、几何间隔和运用拉格朗日对偶性求解的过程进行了推演，推导过程由简单到复杂因此不需要全部掌握，但至少要知道函数间隔与几何间隔在支持向量机中的意义。 \n",
    "\n",
    "接下来，我们就使用 Python 对支持向量机找寻最大间隔的过程进行实战。由于支持向量机纯 Python 实现太过复杂，所以本次实验直接使用 scikit-learn 完成。 \n",
    "\n",
    "scikit-learn 中的支持向量机分类器对应的类及参数为： "
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       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "\n",
    "sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112d3aaf",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "主要的参数如下： \n",
    "\n",
    "  * ` C  ` : 支持向量机中对应的惩罚参数。 \n",
    "\n",
    "  * ` kernel  ` : 核函数，linear, poly, rbf, sigmoid, precomputed 可选，下文详细介绍。 \n",
    "\n",
    "  * ` degree  ` : poly 多项式核函数的指数。 \n",
    "\n",
    "  * ` tol  ` : 收敛停止的容许值。 \n",
    "\n",
    "如下图所示，支持向量机中当训练数据集并非完全线性可分时，这样在保证每个点都被正确分开后会造成过拟合，为了解决过拟合问题，引入惩罚因子  $\\xi$  ，可以看作上面的参数 ` C  ` ，允许少部分的点出错。在训练集不完全线性可分情况下，我们就要使几何间隔尽可能的大，同时使误分类点的个数尽量小， ` C  ` 则是调合二者的系数。 \n",
    "\n",
    "[ ![https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017904.png](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017904.png) ](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711017904.png)\n",
    "\n",
    "当我们使用支持向量机求解这类问题时，就会把最大间隔称之为最大「软间隔」，而软间隔就意味着可以容许零星噪声数据被误分类。而上文中能将数据完美分开的最大间隔也就被称为「硬间隔」。 \n",
    "\n",
    "这里，我们还是使用上面生成的示例数据训练支持向量机模型。由于是线性可分数据， ` kernel  ` 参数指定为 ` linear  ` 即可。 \n",
    "\n",
    "首先，训练支持向量机线性分类模型： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b8f5f44",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "linear_svc = SVC(kernel=\"linear\")\n",
    "linear_svc.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec9532e",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "\n",
    "SVC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb38c48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86300ce6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "对于训练完成的模型，我们可以通过 ` support_vectors_  ` 属性输出它对应的支持向量： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ae111941",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.57325754, -3.92687452],\n",
       "       [ 2.49156506, -5.96321164],\n",
       "       [ 4.62473719, -6.02504452]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_svc.support_vectors_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a2b1e7",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "可以看到，一共有 3 个支持向量。如果你输出  $x, y$  的坐标值，就能看到这 3 个支持向量所对应的数据。 \n",
    "\n",
    "接下来，我们可以使用 Matplotlib 绘制出训练完成的支持向量机对于的分割线和间隔。为了方便后文重复使用，这里将绘图操作写入到 ` svc_plot()  ` 函数中： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7757a38f",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "def svc_plot(model):\n",
    "    # 获取到当前 Axes 子图数据，并为绘制分割线做准备\n",
    "    ax = plt.gca()\n",
    "    x = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 50)\n",
    "    y = np.linspace(ax.get_ylim()[0], ax.get_ylim()[1], 50)\n",
    "    Y, X = np.meshgrid(y, x)\n",
    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
    "    P = model.decision_function(xy).reshape(X.shape)\n",
    "\n",
    "    # 使用轮廓线方法绘制分割线\n",
    "    ax.contour(X, Y, P, colors=\"green\", levels=[-1, 0, 1], linestyles=[\"--\", \"-\", \"--\"])\n",
    "\n",
    "    # 标记出支持向量的位置\n",
    "    ax.scatter(\n",
    "        model.support_vectors_[:, 0], model.support_vectors_[:, 1], c=\"green\", s=100\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "288fa753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制最大间隔支持向量图\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")\n",
    "svc_plot(linear_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93555726",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "如上图所示，绿色实线代表最终找到的分割线，绿色虚线之间的间隔也就是最大间隔。同时，绿色实心点即代表 3 个支持向量的位置。 \n",
    "\n",
    "上面的数据点可以被线性可分。那么，如果我们加入噪声使得数据集变成不完美线性可分，结果会怎么样呢？ \n",
    "\n",
    "接下来，我们就来处理有噪声点时支持向量机的分类过程： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ea3e897f",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e0a43f5bd0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 向原数据集中加入噪声点\n",
    "x = np.concatenate((x, np.array([[3, -4], [4, -3.8], [2.5, -6.3], [3.3, -5.8]])))\n",
    "y = np.concatenate((y, np.array([1, 1, 0, 0])))\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608ac1ad",
   "metadata": {},
   "source": [
    "**----以下代码解释----**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac44662",
   "metadata": {},
   "source": [
    "# `np.concatenate` 方法解释\n",
    "\n",
    "在代码中的这行：\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505f26c2",
   "metadata": {},
   "source": [
    "\n",
    "x = np.concatenate((x, np.array([[3, -4], [4, -3.8], [2.5, -6.3], [3.3, -5.8]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2211f503",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "`np.concatenate` 是 NumPy 中用于合并数组的方法，让我详细解释：\n",
    "\n",
    "## 基本功能\n",
    "- 将两个或多个数组沿着现有的轴连接在一起\n",
    "- 默认沿着 axis=0（第一个轴）进行连接\n",
    "\n",
    "## 参数说明\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e81a742",
   "metadata": {},
   "source": [
    "```\n",
    "np.concatenate((a1, a2, ...), axis=0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6746c6c",
   "metadata": {},
   "source": [
    "- 第一个参数：要连接的数组组成的元组\n",
    "- axis：指定沿着哪个轴连接，默认是0\n",
    "\n",
    "## 代码示例中的具体应用\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2f75a8",
   "metadata": {},
   "source": [
    "```\n",
    "# 原始数组x是通过make_blobs生成的60个样本点\n",
    "# 新增加4个噪声点\n",
    "new_points = np.array([[3, -4], [4, -3.8], [2.5, -6.3], [3.3, -5.8]])\n",
    "x = np.concatenate((x, new_points))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6dbc9f5",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "结果：\n",
    "- 将原始的x数组（形状为60×2）和新的噪声点数组（形状为4×2）连接\n",
    "- 得到新的数组（形状为64×2）\n",
    "- 保持了每个样本点的二维特征结构\n",
    "\n",
    "这种操作在机器学习中经常用于：\n",
    "- 添加新的样本点\n",
    "- 合并数据集\n",
    "- 增加噪声数据进行模型测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05754fb",
   "metadata": {},
   "source": [
    "**----以上代码解释----**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac716142",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "可以看到，此时的红蓝数据团中各混入了两个噪声点。 \n",
    "\n",
    "训练此时支持向量机模型并绘制成分割线和最大间隔： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1c4825bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_svc.fit(x, y)  # 训练\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")\n",
    "svc_plot(linear_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2c223c",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "由于噪声点的混入，此时支持向量的数量由原来的 3 个变成了 11 个。 \n",
    "\n",
    "前面的实验中，我们提到了惩罚系数  $C$  ，下面可以通过更改  $C$  的取值来观察支持向量的变化过程。与此同时，我们要引入一个可以在 Notebook 中实现交互操作的模块。你可以通过选择不同的  $C$  查看最终绘图的效果。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0041d123",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea17c462221b46e4a319cd100a6f17f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='c', options=(1, 10000, 1000000), value=1), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.change_c(c)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AALko6eVqQ4cO1S233BLznGnTpqlbt26htxcsWKDdd99de+yxhx599NGYMzl2eGwmx4IOy9UA5CorLmC/Nn/5RapTRzrkELcZaO3afo8MAIDMXa6WdMhZvHixlixZEvOcjh07qtb/OtQtXLjQCTd9+vTRk08+qWrVEp88Yk8OAAAAgGSzQdLV1Zo3b+4cibAZnP79+6tnz5564oknkgo4AAAAAJBRJaQt4NgMTvv27Z2S0TYD5GlpC8oBAAAAIAVSNrXy4YcfOsUGxo0bpzZt2qhVq1ahAwDgjylTpJNOcosWNGgg9esnPf+8W7YaAICgSGufnGSxJwcAqs4jj0hW4NKqtK1f7z5mr1vAOfxw6aWXpBopm98HACCgfXIAAP749ls34NhtLS/gGG8G5403rIy/b8MDAKBKEXIAIAfcc09pn51ILPzcdZe0dm06RwUAQGoQcgAgB7zzTtkZnEisO8DUqekaEQAAqUPIAYAcEC/geNatS/VIAABIPUIOAOSAHXaIvVzNWA/nrl3TNSIAAFKHkAMAOeC882KXibaqaieeKDVunM5RAQCQGoQcAMgBhx4qDRwYPeC0bSvdfHO6RwUAQGoQcgAgB+TlSU8+Kd12m9S6denjdepIp50mTZoktWjh5wgBAKg6NAMFgBxjy9amT3fLRXfuLDVs6PeIAACo2mxAb2sAyDFWgGDrrf0eBZAaa9ZIL78sjR/vBvpevdylmuw3A3ILMzkAACAQbNnlIYdIixe7e83sCqe42F2W+dRT0lFH+T1CAOnKBuzJAQAAWW/2bGmffaSlS0t7Q9lMjgUdm9059ljps8/8HiWAdCHkAACArHfnnW6YiVQq3YKOFd+47jo/RgbAD4QcAACQ9Ww5ms3eRGPhZ9w4dykbgOAj5AAAgKxmMzUFBYmd6y1nAxBshBwAAJDVbClay5aJVRbcdNN0jAiA3wg5AGKyXirffitNnpz4nVIASLfTT3dDTDT23OGHU0oayBWEHAARrVsnjRghtWol9ewp9enj3gG1C4l//vF7dABQ1vnnSy1auKWjIwWcWrWka67xY2QA/EDIARBxg+6RR7qViMLXrxcVSaNHu4GHoAMgk1jAmTBB2mab0mDjBZ7NNnOLDmy7ra9DBJBGEe53AMh1zz8vvf125OesetHcue4d0fvvT/fIACC6jh3d5bXWFHT8ePeGTa9e0n77SdW4rQvklLySEqtJkv1dTQFUnd69pa+/djuFR1O3rvT331KDBukcGQAAyFWFSWQD7msAqODHH2MHHLN6tdthHAAAINMQcgBUULNmYufZRl4AAIBMQ8gBUMGBB0auUBSubVupS5d0jQgAACBxhBwAFVx0kbthN5ZLL43dkwIAAMAvhBwAFViJ6Pvuc7uIh8/oeKFm0CC3JwUAAEAmIuQAiOjss6WvvpKOP15q3lxq0kTac0/prbekxx+nHCsAAMhc9MkBENWOO0pPPeX3KAAAAJLDvVgAAAAAgULIAQAAABAohBwAAAAAgULIAQAAABAohBwAAAAAgULIAQAAABAohBwAAAAAgULIAQAASKGSEmndOr9HAeQWQg4AAEAKTJsmnX66VL++VKuW1Ly59H//J/39t98jA4KPkAMAAFDFxo+XdthBGj1aWr3afeyff6Rbb5V69JDmzPF7hECwEXIAAACq0PLl0uGHS2vXSuvXl31uwwZ3JufYY/0aHZAbCDkAAABV6Nln3aBTXBz5eQs+U6ZIX3+d7pEBuYOQAwAAUIU+/VTKy4t9TvXq7nkAUoOQAwAAUIVsSVoios30ANh4NargYwAAAmjpUumFF6R586QmTaRjjpE6dPB7VEDm691bevXV+EHIzgOQGnklJVa9PTMVFhYqPz9fBQUFatSokd/DAYCcYH8VRo6URoxwe3vUqOHecbbjhBOkRx6R6tb1e5RA5lqyRNpsM7fwQKSrLFuq1qWL9Msv8Ze1AahcNmC5GgCgjFGjpKuuKr1As6Bjd53t9eefl0480e8RApmtWTPpqafcAGOBJpzdNLC+OfZ/iYADpA4hBwAQYhWhbAYnGpvNef11tzIUgOhseefHH0t77VX6WM2a0vHHu1XVtt/ez9EBwceeHABAyBtvSKtWxT7H7kRbg8NevdI1KlQFm4177z3pyy/dGYRdd5X22Ueqxu3OlNltN/dYtkwqKJCaN3dncQCkHiEHABCycKEbYso3MCx/sWznIXt89ZV01FHS/PnubIItPbzxRqlTJ+m116Tu3f0eYbBZ4Q47AKQP928AACF2pzle+VvbY2DnITtMny71718aTG2PlRdi586Vdt/dfQkAQULIAQCEHH64VKtW7HPsAvmkk9I1Imysm25yi0hECq/22IoV0u23+zEyAEgdQg4AIMSW1Fx6afSqTzaLY7MC/fqle2SojNWr3V5HsZYf2nNPPEFjSgDBQsgBAJRx/fXShReWlr+1PTp2GKsUZdXVKH2bHWzDuy1Pi2flyvgFJwAgm9AMFAAQke3TsCpq8+a5MzzHHSftuKPfo0IyLLjYn894+6zq1HGDDpXWAGSyZLIB1dUAABFtvrk0fLjfo8DGqFdPOuIId/Yt2pI1m6WzBq+ZEnDs1qsFbAtobdu6IQ0AkpUhv9IAAEAq/N//uQEmUoixx6zQxOWXKyM884y0zTZSx47uS6vid+qpbulrAEgGIQcAgADbfnvpnXek/PzSmRvrlWOaNZM+/FDq2lW+u/ZaaeBAadq00sesKpwFH1smSZlrAMlgTw4AADlSae2ll6Qvv3QLR+y2m7uUrXZtv0cmffut1LNn9OctmO29t/Tee+kcFYBszgaEHAAA4KvTT3eLXMQqdW3BbNYsqUOHdI4MQLZmA5arAQAAX02aFDvgGLsl+9136RoRgGxHyAEAAL7y9gjF4/VrAoB4CDkAAsM2Jg8bJvXoIXXvLp15pvT9936PCkA8++/vNp6NF4R22SVdIwKQ7VIacg455BC1a9dOderUUatWrTRw4EAtXLgwlZ8SQI568UWpSxdp1Cg32Pz0k/T4427guf56v0cHIBa7IWHlrG3fTSQWgKzymlWDAwDfQ07//v310ksvacaMGXr11Vc1a9YsHXXUUan8lABykFVmsmaGtqY/vLO7t8b/mmukF17wbXgAEmg8++yzbtAJX5JmoccOq7x2551+jhBAtklrdbW33npLhx12mIqKilQzgQW4VFcDkAi7w2shJtrGZbtw2npr6Ycfot8pBuA/+z9qYea119yS1507S+ecIw0eLNWt6/foAPgtI0tIL126VGeffbYWLFigCRMmRDzHwo8d4V9I27ZtCTkAYmrQQFq5Mv558+ZJm2wiffyx/X5xu6r36kXwAQAgG2RUCekrr7xS9evXV7NmzTRv3jy9+eabUc8dOXKkM3DvsIADAPHYHd9E2H6dli2lAQOk44+Xevd2Z3jGj0/1CAEAQDolPZMzdOhQ3XLLLTHPmTZtmrp16+a8/s8//zizOL///rtGjBjhhJcxY8YoL8KtU2ZyAFRG167SzJluH41obONy+H4dj7fZ+f333Y7qAAAgB5erLV68WEuWLIl5TseOHVWrVq0Kj//xxx9OaJk4caL69u0b93OxJwdAIu66S7r44ughJ1rACQ86tvH5t99YugYAQKZKJhsk3VarefPmzlEZxcXFzsvw2RoA2FhDhkhPP+2Wji4fZizg2D2XtWujBx371TR7tvTpp9Iee6RlyAAAIIVStidn8uTJuvfee/X99987S9XGjx+v448/Xp06dUpoFgcAElWvnruvxqqslS8/u88+0rbbxp7J8fz6a0qHCQAAsj3k1KtXT6+99pr22msvde3aVYMHD1b37t316aefqnbt2qn6tABylM1aP/GEZP2G33hDeuUVadYs6b33bAbaXZIWT/366RgpAAAIVJ+cZLEnB0BVeOwx6fTTY59jrbv+/JOO6gAAZKqMKiENAH6zctFWOtr250Riszy2r4eAk/lmzHBn6d56S1q2zO/RAAAyFSEHQE7s2fnoI7cRqO3T8SqoeaHH+ubccYevQ0Qc06ZJu+8uWXeCo4+WDj1UatVKOvfcxPskAQByR9LV1QAgG1nTTyss8Mwz0osvSv/+K22xhXTGGdJeeyW2Zwf+sH83q1ezYkXZx61Q54MPugFo7Fh3ySEAAIY9OQCAjHbYYdKYMbEr5Fl4PfFEZSX7K/zdd9KiRVKLFlLPnvRrAoBI2JMDABmmoEC6917pqKOkww+XRo6U/v7b71FlPrvwt/038Zq5PvCAspJ9bVtu6QYbWzbZq5c7w2j7jgAAlcdMDgCkmO0HsmCzcqV7h95+69pL2xP0+OPSSSf5PcLM9cUXUr9+8c9r2lRaskRZ5dln3X9772fC47396KPS4MF+jhAAMgszOQCQIWy/yEEHSatWuReuxcWlL9etk04+2W1kiuhFIxKRbT2ObH/RmWe6r5e/1ei9fd557gwgACB5hBwASCGr2mZLrSzURFtqdcMNChybVbn9drcKmoW8m26S/vor+Y/TvbvUpk3sc2xGzCquZRMrfmHBNxYrrGB7jQAAySPkAEAKPf+8tH599OctAH38sfTPPwqMt9+W2raVrrjCff2dd6Srr3Yfe+655D6WBZihQ6M/b0u7atRwS0lnk19+cccdiz1v5wEAkkfIAYAUsQBj+3ASEZRlSd9/Lx1xhLRmTenSPOMtz7M9KJ99ltzHPOcc6eKL3dfDg4HNgtWpI73xhtSxo7KKjTvejlh73s4DACSPkAMAKWKzEC1bxj/P+rtsuqkCYdQo92W0C3gLJlZZLhk2W2PL/iZNcstEb7uttOOO0vDh0m+/Sfvvr6xjS/hizfAZe/7gg9M1IgAIFqqrAUAK2YX4jTdGL4FsMxPHHSc9/XS6R1b1bLbGZh5sxiZeaLFmrLn8a93+8vbpI337beSwYz8XW23lzozRMwcAXFRXA4AMcf750mabRd5/YTM9Vj3smmsUCLZELV7A8S7wly9XTrPg8vrr7jI7e90LMt7r7dq5DVAJOABQOYQcAEihTTaRJkyQ+vZ137aLVluyZbp1c5/r0kWBULeu268mHpvtadYsHSPKbK1bS999Jz30kNS7t1uYwZbh3Xef9MMP7tsAgMphuRoApMmPP0qffOIuXbOLWgs+QbtTP2yYuy8n1vI8a3D54IPpHhkAINslkw0IOUAafP21e3fWLnDtonb33d1Gfz17+j0yoGotXiztsIO0aFHFvSa2PC8/392H0r69XyMEAGQr9uQAGeSWW6RevdymfnPnSnPmuK/bspTbbvN7dEDVat5c+uILaeed3bfD95v06CFNnEjAAQCkHjM5QApZI8RDDol9zrvvSgcckK4RAekzdarbE8f+ytjSPJvhAQAgHdkgTr9lABvj1lvdJTrR9ifYc7Z/gZCDINpmG/cAACDdCDlAiqxY4VbOisXCz8cfu6V3s62zeWGhu+zOxm/9UXbaSTr1VKlFC79HBgAAch0hB0iRoqLkzs2mkDN+vHTYYW6Qs/0WthzpjTekq6+WHn9cOukkv0cIAAByGYUHgBRp0sTtkRLPpptmV+f36dOlAQOklSvdcGOzON5LawR58sluCAIAAPALIQdIEWv4ePbZpY0fo51z7rnZ1Svljjvc0sAWaqJ9TTfckO5RAQAAlKK6GpDifStWStdmP8oXH7CiA1tt5ZbbbdhQWaNBA3cWJ5F+KYnMZAEAACSCPjlAhrD/f59/7i7hqlWr9HF7fdAgt7xuNgUcC2qJBBzz77+pHg0AAEBkzOQAabJ0qdvp3ZamWb8Q27OTjVq1crvZx1Kjhvv1ZlOAAwAAmY2ZHCADNW0q7b23tNde2RtwzBlnuEvtYgWc444j4AAAAP8QcgAk5fzzpdat3TBTnoWfevXcUtIAAAB+IeQASIoVE7Amp336lFZT8yrIde3q7jPaYgtfhwgAAHIczUABJK1dO7egwg8/SJ984hYk2GknaZddsqscNgAACCZCDoBK22479wAAAMgkLFcDAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAkPGKS4oTPpc+OQAAAAA22oq1K7R09VKtWrcq4nFglwPVuE5j59xxs8fpvd/e0+p1q7VqfcVzHz34UW3dYmvn3Lsm3aUrP7pSRSuLEh4LIQcAAAAIoJKSEq1Zv8YJDU3qNlG1PHcR18wlMzX337lavX51xDBySd9L1Kh2I+fcJ79/Uq9Pf915fOXalc7L8Pf76vSv1KlpJ+fcmz6/SSMnjIw6nm/P+FY9WvVwXv9qwVe6/cvbo567ZPWS0Os27qINiQccQ8gBAAAA0hw+7KI9UsDYue3OofNstmP6P9Mjz4ysX6UnD31StWvUds69evzVevmXl0PPr/5fEPEsvnyxNqm3ifP6fyf9Vw98/UDU8Z2y3SmhkDNt8TS9NeOtqOeuXLcy9Hq9mvVUp0Yd56V31K1RN/S6Pefp06aPLt/58jLPhx9bNd8qdO7J252sQ7sdqvWr1qvTzW6gioeQAwBAQKxaJb38sjRjhlSvnnTYYdI22/g9KiA7/b3ybxWsKSgTLLzgsKF4g47f9vjQuY9884h++vunCufZsb54vb4c/GXo3KNfPlqv/vKqSlQS8fOu/c9a1axe03n9se8e0/NTn486xvsPvD8UchavWqwZS2ZEPXdVWOBpl99O3TftHjVg1K1ZN3TuEVseoc5NO6t+rfqh0BL+eocmHULnXrXrVfrPbv9J4Lsr9e/Q3zkSkV8n3zkK8wqVKEIOAAAB8NRT0nnnScuXSzVrSsXF0tVXS/vuKz3/vNS0qTL6rrYtTbH1/A1qNVCzus2Ul5fn97CQYT8j64rXORfq6zasU/P6zUPPTfpjkpasWhJxtsMu1i/b+bLQuZeOvVQ/L/454rkNazfUrAtmhc496qWj9Pm8zyOOxy7ww0POGzPe0Lsz3406fgtF1atVDy29Cg84NavVLBMwbIbHCzk222Fft/NcjbIhxF7Wql4r9HEu6nORTtj2hIizKHVr1lWNaqWX/UP7DXWORPRu09s5EpFJ/2/zSuynJkMVFhYqPz9fBQUFatTInTIDAABl2ezNMcdEfq56dWn77aUvv3TDTyb5d82/Gv39aN3z1T2ataz04rJTk046f6fzdcr2p4Q2KSNzWeiININhF+Dbt9w+dJ79W1uYLTMz8r9N560atNKt+9waOnf/Z/bXr0t+LXPuhpINznNbNNtCM84rnbHo/kB3ZxYlktYNW2vBJQtCb+/y+C6aOH9ixHMb1mqowmGlMwUHPXeQE3IqzHL8byZjzPFjQhf19rXNXDoz6jKtfTvtGwo5i1cudr4W7xwv0KBqswEhBwCALJ65sBmbjh2l33+Pfd6LL0YPQn4Y+9tYHfnSkaElNOF3tvPkfi/sIvDVY17Vfp33822cQfl5m/PvHDdQRJjBaFG/RZnv8RUfXqHCosKI52636XZ65JBHQuduetumzrKuSHbabCdNPn1y6O32d7bXvIJ5Ec+1/Rc/n/Nz6O1t7t/GmXGJpH1+e829aG7o7RNePUG/Lf2twnIrm/mwPSg37nVj6Nwxv47RstXLQjMh5Y+OTTom/H1FZmcDlqsBAJDFMxcTJ8YPODab8/jjmRNyLOAMeG6Ac/EdaV+C95hdlNt575zwTqCCju3R8AKHLSFqVq9ZaEZk3JxxEcOFnW9BwFsiVbS+SEe8dETFWZH/vb5/5/31wlEvhD5n57s7R90DYrMM4d/fh755yAk5kYQvefKWWoWHU5vh8GYwWjZoWebcQ7Y4RMvWLCszG+Kdu2mDTcuc+8ShT4RmO8of4Z/TPHfkc0rUQVsclPC5yG6EHAAAfJi5CDd72WxdPPZiXTX+qqRnLv78M/45GzZI8+crY4KefR8s4BQrdmM/e75aSTXn/D8u+SPlS9es0WCk2Q5bgmUX7Lb52thm9NE/jHbK6UYqwbtPx300pOeQ0NKkvo/1LbOUa+2GtaHPedr2p+mxQx9zXrdSvwc8e0DU8R291dGhkGNLnGLtAQkPKTZLaHtY7Hsevp/DCxo9W/Us875W8cq+F5EChs36hPvmjG+cZWne/pBYM5L3HHiPEtVrs14JnwtEQsgBACCLZy42cSvCxlStmrRp2RvlvrGZLLvQjzarECno2PlP/fCULuh9gfM9+r3g94gzHfbSenDs0GoH531tadQtE26J2gtkcI/BOr/3+aEyuVvdX1qytrxL+lyi2/e7PRQgLnz/wqjnWhjzQo6FkfBZu/JsU7nHwkePlj0ihgs7vK/L27zulQ+OdG75QPjXZX8pUYlWxzLlZ2CATEHIAQAgi2cudt1VatlSWrQoxsctlk45RVXO7vbb7IMXMGy8VqHK/LXiL6fqVXjAsJmPW764JeGA47Hz7558t7Ok77tF3zmbx6MZsceIUBiwGZf7v74/6rnzC0unt8JL5pryvT6a1i0tT2elbI/d+ljn8fo161fY37Fti23LbGb/4rQvKmxE93qGhM982FKwb8/8NuHviy1vBBAZIQcAgAyeuYinRg3p+uulIe7Ewf+USDXWSDVXq3qdVWrTYZW67r5KE+e7YcP6Y3jLjmwGwzZjR1qi5XU+3639bs657818T2eOObPMOeEeP+RxndrjVOf1b//8Voe9eJiqis2GLF291CnU0KROkzIbzMODhrekzLRq2ErX7HZNmSVa0TaZt23UVn9f9nfoXK8zfCTWJDF8v0ssVlErvLkjgPQg5AAAkCI2e2NFBpJ+P5Xo+s+u15FbHqnNGm3mPPbxnI/13E/POeV2Iy29OufmR/Twf/o5szbq+aiKB5zhvJ8V3bW6BH1Hl3582/djDf7M94u+1xUfXRF1LHaeF3JsI3j47Ec4248RvvTK9oBYj4/wGQyb+Xl12quqrOVrlzsBbemVSxM63yprjeg/IuEwEt57BUB2I+QAAHJeeKNBb3+HdQT3+lf8svgX/fx3aQPB8ns8rtzlSmfWwNgMzKPfPuqcs7xoecz9GLH8s+offbXgKx3e6HDnbetk/uh3j0Y9/5bjluqaQdLo0dK78+vp0yiNBr3Q4bGZj5O3O9l5zGYv7GI/v3a+ExBshsSCiqdfu36aMmRKxF4gXg8Qz46tdyzT5d37mjYm5NjSLwBIBCEHAHxAh/fKNRqMFDCskpUXRj6Y9YETDMpUyAqb+Xj68Kedi3cz4pMReuDrByo0GvT8et6v6tKsi/P6sz8+q5sm3BR1jBYSvJCzcPnCqF3Sk2WbysN7jtzQ/4aKgeV/y7W2abGNmtWTrrhCunD9UVq74ZAKXc4jsa9xh5Y7RC1rHV5Ny/bcWHipLPs5t49r1eSS2ZdjpYltaVn4vhgAiIWQAwBpFKQO79brI/wCes6yOVq8anHUhoMX9bkoFOQe+vohfTH/i6jBxZoC2qZsc/rbpzuzI9FY1SjvQvzN6W/G3GhuG9G9kGOf86+VFStO2WyGhYeiDUWhxzo17eQs2YrWzbx5vdJlTod2PdSZHbHHrVTw4S+6MzGV0Xuz3qHXbTN9eHWteOEoPCClu6x1NPbvbz/n9nGTZfuTuBEAIFF5JXY7MQBdTQEg06Wrw/uG4g1lQoPNhHgzEuaTuZ9oQeGCiEHELiJv2/e20LmXfXCZJsybEPXcov+UBoFDXzhUb814K+q47Fzbt2FOeu0kPfvTs1HPXXLFktBd+7PHnK0Hv3kw1GiwfMB4+/i3Q7Moz//0vD6e+3HESlZ2HNbtMKcylvmj8A9nI3t4vxCv0WBVXUzbn9gu93Sp9MzFzPNnpuzCPrysdayqb9VUzRlDVTXktKDf5o42ThiOV23O+fx51Zx/n3T0yQGQ2ZLJBoQcAEgD74JyQ4ld1JUkdEFpvUDsQjzSbIftmXjmiGdC77f/M/tr8oLJFRoNGpvlCO+RsceTe+jT370dG2XZxeSqq0rv6h/03EF6Z+Y7Uce77up1odmcs8acpfd/ez9iFSs7Hjn4EeelsWpe0/+ZHnFWxA5bEuUtQbOLYbvQjddoMFPdNekuZ+Yi2ZBz5/53JlRdLS1BQ9Wcf9eqChrJBqx3T3xX+3bad6M/L4Dslkw2YLkagJxnFZ9stiN8eY9VnLL9MuUbDdrRrF4znbDtCaFzL3r/Ii1asSjibIctc3r+yOedGZxi555SScJ9UjZvvLl+XvxzxPOs+3o4G6tduJZn4cFb9uXxAkQoVNQo3dth+4PCDes3TGf0PCNqc8LqeaWbzR886EEl6qAtDnKORJTvX5JtbAmiLflKdubC9vlka1nreGxGyIJ8vJlN+7d/7djXCDgAksZMDoCMZb+erNFgeGiwC/b2jduHnn/x5xcrBJGV61Y6L7s26xrqZm76j+7vBIHyQcQ+x54d9tS4k8eFzm12azNnKVMkvVr30ldDvgq9vfmdmzuzLpF026Sbzup5VqXu5B/Q5QB1aNwh4j4Qu5t+5FZHhs7/belvzh4Z24DuBRb7XsXq9ZEOFFjIvJmLTFpCZ/8fLThZo8/ye9QsTJ2y3Smh5YUAUMhMDoB0zH5ECgxe2GjdsLV6tu7pnGtvj5o4KuK5duzefnddtdtVzrkWONr+t23oufIO73a4c2fXY3s7ylfF8ljVrfCQ892f36mgqCDiueU/l11k2QV5+SpW9rJL09L9LeaqXa9yxh1pmVajWo10xEtHJNnf3TXjnxkac/yYhC4owxsgZoIgFVioCpk0c2GhszJlrW28XkNOm82sCvYzYGHGfibs41ofHCsTbfuxcjEMA6g6hBwgQOwOrbcfw1t6ZRdUP/71Y9SA0bNVT/Xv0N85968Vf+mSDy6JuETLjhO3PVE37nVj6NzWd7SOOpZTtz9Vjx/6uPO6hZDhnwyPem74ndra1Ws7vTTKs/0YFhrCl17ZRdD+nfd3Xg+f6fA2kG/RbIsyH+O5I59zlldFWnZl7xMufKYmniE9y7SaL8O+lky5oEyXdFfsyhb2tdqelkgzFzZDkq6ZC5tV2xgWRKr6Z9L+L9vHzLafdQCZi5AD+NBo0DtsX4VX+vbvlX/rvZnvVSil6x1WlnbAFgOcc23Dts1gRDrXZliu3u1qXdf/Oufc3//9XX0f6xt1bBf1vigUcmyM1lE9Ghujx9tAbpWowmcwvJBh+0k89tiZPc+Muq/DlmSFX+xY+eDwjeixen2MOWFMwv8OB3Y5UOmWiReU6VqWFWkplPeYBWg7r6oqdmWLTJi5KL/vKlk05ASQDdIScoqKitS7d2/98MMP+u6777T99tun49MCCbPZD7vgiDXb0aFJh9ASIrsTG6nRoB0X97nYKVNrPp37qQ5+/uCIjQbN7fverkv6XhK6wz3ozUFRx7hZw81CIcf2Xnzz5zdRzw2/g96wdsMK+zrCD29JmbELLRuTt6+j/DKtVg3cMr2mUe1GWvuftaEKWLFYJ/RkNqVv1XwrBUUuXVDaEjWbwYm376R8gYVcLA3s58wFDTkB5IK0hJwrrrhCrVu3dkIOkCi7kLe7vbY8ybuQtiVSNosRbbbjqK2OCi1R+vz3z3X3V3dH3TNy/4D7Q2HEGgge88oxUcfy2CGPhULOnH/nxOx8fkS3I0Kv2+yD3akNZxvBvRBhsyAeayZoS6+i9fewRoQemyWxO+DlZ0/spdf93NOmURvNvnB2Qt9z+1he6ErkIi2RgJPrcumC0u+KXUgMDTkB5IKUh5z33ntPH3zwgV599VXndWS/8o0GvWPLTbYM7Wv46a+fNOmPSZEDxvrVTlnarpt0DTXvGzlhZIXzbOmUGXvS2NAm3Ld/fVtD3o6+/8GqaXkhZ8HyBXrll1didj4Pv7i3i8rwmYvw2Qy7UPXYheeFvS+MGkZ6tOoROte6k1slovDnozUatFLD7534XsKzA34svULycuWC0mZvrMhAZdj+FPseZcvXGgSZWNYaALIm5Pz1118aMmSI3njjDdWr567fj7eszY7wMnFIXmFRoZatXhZ1tuOQroeEltBY476PZn8UsdmgHc8c/oxz8W1Gfj5S1356bYVGg57Jp0/WTpvt5Lw+dtZYXf7h5VHHOLD7wFDIsfH+9PdPCS29stkOK8kbLWC0zW9bphfIvQfcG3WZlleG2Fip3g3XbEjoIstClDXpS4SFpUyreoX0y4ULykyq2IX4bHmgFX6wfVG2bDBuWWvlOVXfcm1ZIYDsVSOVd/UGDRqks846SzvuuKPmzp0b931GjhypESNGKGhsI7jX6yO8R4Qtu5pfMD/qPpCh/YaGmuA99PVDTtfxaOdOPWeqU7LXXPPxNbpr8l1RxzPjvBmh2Y4v5n2h27+8Peq5y9YsK3PhVT7ghAeN8JZLNqNy8BYHu1Wu/tdoMPyw2RCPzUh8OPDDCr1AvOVX4dW0Du12qHMkwsJF550SCxh+9xJBsOXCBWWuFVgIgkwqaw0AvoecoUOH6pZbbol5zrRp05wlasuXL9ewYcMS/th27iWXXFJmJqdt29I786luNOjNYthjfdr0KVMtaObSmRXK6Xrv8+wRz4aCy2UfXKYxv44JNSO097FzPCv/b2WoKtXNE27W6B9GRx3fOb3OCYWcqX9PdZZqJTLb4ZXZDS+lG36EV6natf2uunzD5RFnOuz9bS+Bx7qeW5f3RBoNHtz1YOdIhM2+hM/AAEEU9AvKXCqwECSZUtYaAKpaXkn47fcELF68WEuWLIl5TseOHXXMMcfo7bffLrP8Z8OGDapevbpOPPFEjR4d/eK+fFfTb+Z8o+p1qlcIGPaxj9m6dLP4fV/dpxlLZkSc6bAN0h+f8nHoXLujauV6I22QtRCw7mp3P4g57IXD9OaMN6OOc81Va0I9SQa+PlDP/PhM1HMXX75Ym9TbxHn92k+u1evTX4+8nKpGPV2/5/WhTcc242IzP+GVrsrPjFgfEWP/pKxtBzJTUDu82++dLvd0qXSBBdu7xu8t//8NacgJIJN52aCgoECNGjWq2pCTqHnz5pXZU7Nw4ULtt99+euWVV5xy0m3atEn4C9FQSaUrlkIsLFho8PQf3V+fzP0k4seyBoNr/rMm9LaV9bUZF48FhPClV7aky8remlFfjNKUhVOi7gM5a8ezQiHn579/dtaml29K6L3tfUwAuS2IF5R3TbrLKbCQbMixPW5UVwMAZEXIKc/25HTo0CGpPjneF9JoeCPVb1i/wgyGXRTYOnfPI988ot8Lfo+6t8M2l4c3NbQvPV6jQQBA4rNUbe5ok3SBhVzskwMASG3IyYor+/mXzI/7hZghPaOXFi7P6zIPAKgauVBgAQCQHdJWUmrzzTd3Zk4SncUBAGRvgQWbIbcQ4xVU8HiP2fPvnvhu1hVYAABkB+rmAgBSUrHL9tqEl4s39rY9vuCSBQQcAEDKpG1PTqrX3QEAMk8QCywAAPwRuD05AIDsZIHGmnzS6BMAkE4sVwMAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKIQcAAAAAIFCyAEAAAAQKDX8HgAAAACA4CopKVHRhiKtWreqwrF63erIj6+v+HhBQUHCn5OQAwAAAORo+Fi7YW3ccBEzkKxfldD5JSrZ+AGvSfxUQg4AAACQYdZtWBc1PCQy67EqwfcpLilO69dVo1oN1a9ZX/Vq1nOOujXrhl4vc9So+JiKpItuviixz5PyrwQAAAAIUPhINFQkMvMR6Rz7+OuL16f166qeV131a9VX3RpRQocXSmrUdc4LfzvqueUCjJ1bs3rNSo+xsLBQF4mQAwAAgBxhoSDa/o5kZjzinZPu8FEtr5oz8xEeGJIJGvXKPW8fJ3wmxTs2JnxkIkIOAAAAUmZD8YaowSFeKPFmPhI5b13xurR+XXnKixksYgWQaEEj0sxHreq1lJeXl9avLQgIOQAAADnI9mLECg9xN6DH2HAeHmCsqla6VSZ4JLP8ys6pU6MO4SODEXIAAAAyLHysWb8m+RmPJDeh+xE+vABRfiajzAxGhA3n5c8pv1wr/KhdvTbhA4QcAACARMvthoePeGEi1qbzeDMo6WazElU54xEpnNjnsP0lQDoQcgAAQM41Gkx4WVa559LNZiWiBYcysxkRZj+iluYtF1LsPMIHgoaQAwAAUhY+bDN4wsuskmg0WP7cKmk0mATbDO6FjI3d3xHrfatXq57WrwsICkIOAAA5KFqjwYSXYMXZdO6du6FkQ1q/rprVasYMEOVDR7S9HfHCiTU0BJC5+B8KAEAGsR4cSc10JFlu1wsxfvX6iLnkKsam80SWbG1so0EAwZHSkLP55pvr999/L/PYyJEjNXTo0FR+WgAAfGk0GGsmJG45Xh97fZSfyUh0Q3ki53ozKjbDQsUrAIGZybnuuus0ZMiQ0NsNGzZM9acEAOSQaI0GK9v/I9q5azeszZhGgxVK6UaYIUk0lNBoEEAQpTzkWKhp2bJlqj8NACALen0ktQRrfWLnZkKjwfJLppKd/YjUM4ReHwBQeXklVvokhcvV1qxZo3Xr1qldu3Y64YQTdPHFF6tGjcjZqqioyDk8hYWFatu2rQoKCtSoUaNUDRMAcrrXR2VmORI5xz5HukUKGBVmNWpE7l4erbFg+WBCl3MA8Idlg/z8/ISyQUpnci644ALtsMMOatq0qSZOnKhhw4bpzz//1B133BHxfNuvM2LEiFQOCQAC0Wgw1uxHvH0jfjQajNbro3zYsNmMZPaDhG8+p9cHAKDSMzlWNOCWW26Jec60adPUrVu3Co8//vjjOvPMM7VixQrVrl27wvPM5ADIRPZr0vZjbOz+jkRK86a710d4+PCWTEXazxEteIQ/Xn7JlXfYzAe9PgAA6ZzJSTrkLF68WEuWLIl5TseOHVWrVq0Kj//888/aZpttNH36dHXt2rVKvxAAuRs+ElpeFWffR7zZDz8aDcbb3+GEkXJLryKFjGgb0Wk0CADIJildrta8eXPnqIzvv/9e1apVU4sWLSr1/gCyv9FgwsuwEmg0aIdtbk+n6nnVozYOjBUyEpkZCZ9JodEgAACVl7K/ol9++aUmT56s/v37OxXW7G0rOnDSSSepSZMmqfq0ACrRaDChBoNJlt1Nd5dzr9FgeNCIWEY3RqPBiI0JywUTGg0CAJDDIcf23Lzwwgu69tprnX02HTp0cELOJZdckqpPCWR9r4/KhIlEZj78bDRo4SOp2YwES/GWDzA0GgQAACkPOVZVbdKkSan68ICvjQY3tslgpHP8bDRYobFg+ExGhHK78ZZhhb8/4QMAAKQbi76RtWwvhoWNRCpWxdpwHu/9/Gg0GG3JVLzSurH2hZQPKDQaBAAAQUXIQdobDSa7HCva+ZnQaDDano9EZjyihRcaDQIAAGwcQk6Oho9KNRmM935hz6WbBYNE+nUkMhtSvspVePig0SAAAEDmI+RkeKPBZGc8Yr2PH40GvV4fSZfSjbBcK1o4odEgAAAAwhFy4oQPq0SV6CxHtH0f8d7HQkm6e31Y+IhXucoJGkk0FiwfRuj1AQAAAD/UCFKjwaS6nkfYdB4pjKS710d4o8HysxmJLLNKZOaD8AEAAIAgy4or3d6P9NbammWXc1lDQz8aDcad1ShXbjdWU0IvxISHERoNAgAAADkQcqb/M12qk1yjwfAlUxHL6SbQZDB8JoVeHwAAAEB2yIqQ89bxb6l50+YRw4ntLSF8AAAAAMiqkLP75rurUaNGfg8DAAAAQBag6QcAAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAgUQg4AAACAQCHkAAAAAAiUGn4PAAAAv5WUlGjJ6iVasXaFGtRqoGZ1mykvL8/vYQEAKomQAwDIWf+u+Vejvx+te766R7OWzQo93qlJJ52/0/k6ZftT1LhOY1/HCABIXl6J3b7KUIWFhcrPz1dBQYEaNWrk93AAAAEy9rexOvKlI7Vq3Srn7RKV/jnMkzuLU69mPb16zKvar/N+vo0TAJB8NmBPDgAgJwPOgOcGaPW61U64CQ84xnvMnrfz7HwAQPYg5AAAcm6Jms3g2EKGYhXHPNeet/PsfHs/AEB2IOQAAHKK7cGxJWrxAo7HzrPzn/rhqZSPDQBQNQg5AICcYbMyVmSgMu6efLfz/gCAzEfIAQDkDCsTbVXUyu/BicfOt/dbunppysYGAKg6hBwAQM6wPjgbY/na5VU2FgBA6hByAAA5wxp9boyGtRpW2VgAAKlDyAEA5IxmdZs5jT69PjiJsvPt/ZrWbZqysQEAqg4hBwCQM/Ly8nT+TudX6n0v6H2B8/4AgMxHyAGAKmBVt/5Z9Y/m/jvXeUkVrsx1yvanqF7NeqqW4J/AannVnPNP3u7klI8NAFA1CDkAsBGsQeRdk+5Sl3u6qPmo5upwVwfnpb1tj9NAMvM0rtNYrx7zqjMrEy/o2PO2VO21Y19z3g8AkB3ySjL4dmNhYaHy8/NVUFCgRo0a+T0cAChj7G9jdeRLRzqNIk14WWJvz4fNANgF9X6d9/NtnNi4fz8LOPt22te3cQIAks8GzOQAyDlVsbTMLpAHPDdAq9etdi6Oy/dd8R6z5+08Ox+ZxYLnH5f8oTv3v1Mdm3Qs85y9bY8vuGQBAQcAshAzOQByhi0dG/39aKfjvTV29FjVLNuMbns1ElmSZB+nzR1tnABTrOK459uSp7o16zoX1Cx5ykz2p9AafVofHCsTbVXUKDIAAOmzbsM6Z2bdOzZrtJnq1KjjPDdzyUz98NcPWrJsic7qd1ZC2aBGmsYNABm1NCnc7GWzdfHYi3XV+KsSWlpmQck+TvnZm2gsCNn5T/3wlFOhC5nHAk2zes2cAwhSeF+yeonTBNd6RFkJdcI7krW+eH2Z8OEddqPPXu7WfjfVr1XfOfez3z/T579/XnrOevcc77jvwPvUoUkH59w7J92pmyfcHDrHPk+4SYMnqXeb3s7rb814S5d9eJm0JvFxE3IABJ63tMz+4EcKJt5j3tKyd054J2rQsY9hM0GVcffku50ZIy4yAGTDrDUy24biDU71R+9vyh+Ff2jRikURA8mqdat01o5nhWZGnv7haX04+8MKIcQ7vjjtC7Vs0NI59/IPLtedk++MOo5p505Tt026Oa9/OOtD3fD5DVHPtSXiXshZu2Gt/lr5V9SKlva8p33j9tq13a6qtb6Wxmmc/yHnnXfe0XXXXacff/xRderU0e6776433ngjlZ8SACr8sbcZHAsn8ZaW2fPVSqo550dbWmZ3RcMvGhJlQcrez5ZEMVsAIBtmrVE5xSXFoVkOO9asX6Oum3QNPT9x/kTNWTYn5myHF1xGfDJC7896P2IQsRCwYtiK0CzK1R9frSe/fzLquI7b5rhQcJmycIqe/vHpqOfa7J/HAodXkMVet+XX9WvWd17a2+HNlXtt1kun9zjdedw7vPPs2Lzx5qFzrSz//p33V90apc/bUat6rQo3A4/a6ijncLaynJHvb8h59dVXNWTIEN10003ac889tX79ek2dOjVVnw4A0rK0LPwXf2XYng9CDoBMn7UOKvvaV65bGTE02Pct/Pthsx2/Lf2t9Jz1pefabMObx70ZOve4V47TB7M+cJ4r2lBU5nPWqFZD665eF3p71MRRemN69Jv+d+x3R2jGxW6OTfpjUtRz7fN5IadFvRZq26htmcBQL+ywcXgO6XqI2uW3qxAwvGOzhpuFzr1696udo3b12nFXItjHtSMRFri80JU1hQcs0Gy++eYaMWKEBg8eXOmPQ+EBABvDfr1Zvxq7e5loyDF2V8qqa808f2aFX+g21W59cCrrn8v/IeQAqHLZXBDFZj5s03ntGrVDj039e6oK1hREDCN2UX9aj9NC51417ionDESaGbF9SBMHTwyd2+uRXvp64dcRx2Hn/nPFP6G3+4/ur0/mfhLxXJttKPpPaZg55PlD9Pavb1c4z8KKhYZFly5Szeo1Q7Mzn8/73Pk6nJmOckFjaL+hoZAzZcEULVy+0Hk80vlN6jZxAleuKEwiG6RkJufbb7/VggULVK1aNfXo0UOLFi3S9ttvr1GjRmmbbbaJ+n5FRUXOEf6FAEBlpWJpmf0RtHXtlQ1OVrULAKpaKgqi2I0im5UIDxg1q9UM7akwr017LWoYsepYdsHuOfSFQzW/YH7FDezrV2v7ltvruzO/C517+IuHO7Mokdjv4PCQ895v7+m7RaXvG65F/RZl3vaWXllIsSVX4eHCAkO4Q7Y4RFttslXEmQ4LiPb98W6E3bX/Xbp1n1vLnGNBJVIAGb7HcCXKln+hclIScmbPnu28vPbaa3XHHXc4szq333679thjD/36669q2jTyH/mRI0c6sz8AUBVSsbTM/qDZxl1b154su5Cg6ACAqmAX2DZ7YyFh5dqVum3ibUndeHE+hko09KOhzoZ1u0D3tPtvO+cmT6TQtHv73fXJoNLZjbPGnKXFqxZH/Pg9W/UsE3J++usnzfl3TsRzy+8h8vZuRAoYrRu0LnPuxX0u1rI1y8qGkP/NdlhVuXBjTxrrBLXq1arH/f5c3Dfx3/PhwQ+ZIamQM3ToUN1yyy0xz5k2bZqKi91p0quuukpHHnmk8/oTTzyhNm3a6OWXX9aZZ54Z8X2HDRumSy65pMxMTtu2bZMZIgCElP/jlizrlxKJVSayjbsJLwvJq+b8wbVNlgCCHTzWFa9zll95y41sc/i3f35bpuRu+LFl8y11YJcDnXMtrJw55szI5XrXr9aBnQ/UfQPuC33cprdu/Mywfdzyez5sz4od4SwYeAEi3J4d9nRuCIUCRo3SoGF7PsI9fPDDTplgL4R4y68ifdwPB36Y8NcwcLuBCZ/r/bsg+JIKOZdeeqkGDRoU85yOHTvqzz//dF7faqutQo/Xrl3beW7evHlR39fOsQMAqkKqlpbZ+nWrTGQbd60aW6ygY+ve7eO9duxrvq97B3JZ+UaDzn6Nes3UuqE7K2CzItaLI1o/kL067qVjtj7GOdf2SNjSq0jBZUPJBp3X6zzdc6Bban7Z6mXq+1jfqOOymx9eyLGZ3md/ejbquYtWLgq9bsutnPLB/6t4ZUGjss7e8ewyb1v5YPv44bMi3n6S8l446oWEP8/eHfeu9BiBlIac5s2bO0c8PXv2dMLKjBkz1K9fP+exdevWae7cuWrfvn3SgwSAykjl0jKrwGOVicJLtYYHKa+kpq3btoCzb6d9K/U1ALkSQLzlUZFmMLZuvnWoBO+8gnl6+JuHo/YCsb0a3qzpD4t+0G5P7hax0aC5aterdMOebk+Pv1f+rVPeOCXmDIAXcky0zevll17ZbEWHxh3KlNENDw87t9m5zOe4fd/bo1bHal6v9BrMfj+tuWqNEz42tiBK+d9PXs8TIJulZE+OVTs466yzNHz4cGe5mQUbKzpgjj766FR8SgBI+9IyCzpWmcg27lqjz/AiBzYTZEHplO1OUX6dxGr6VwU6nKMqGw1awLA7+naYJauW6OfFP1foeO4dNiOxXcvtnHNtidbICSMjLtGy48Y9b9SpPU4NdUnf++nod/lH7TPKDTnFxVr00Ru6ceqNUc+1PSMeG3dhUWHERoN2WElcj8207tdpv6gBo/dmbud1s0m9TTTm+DGhfiGRzvfY/8PZF7p7leOxsV3St3TZfjze7AoFUYA09smxUFOjRg0NHDhQq1evVu/evTV+/Hg1aVK2cgUApFKql5bZeRZmbMbI7kTbkhHby2MXDekMF3Q4z73wUT48dG7aORSoZ/wzwwkO0bqZ28/sDq12cM59d+a7GjZuWMRGg+b5I593mgiaT3//1Jm9jMYu/r2QY7MLr/zySsyfWY8XCqIFDKeXxrhx0mmnqfW/83R+vzzVW1uieuukultuq/rHn6J6jZo5527TorSKa6emnZxS8OEb0SM1GvSqcL1/0vsJ/RvYxxiwxQBlCgqiAGnqk1NV6JMDIFVdwCMtLbMLoGxcWpbo10aH8/SwWbRoS6/ssNkCr2qfBZG3Z7xdptFgeHixzuc9WvVwzn3w6wd10fsXVWg06HnvxPec7uHm8e8e1+C3ovepe+mol3T01u7Kipd/flnHvFK6DKu8xw55LFSu13qGWDWt8DK6oQ3kNeo5G8D32HwP51yr2PXm9DcrdDz3jjaN2jihyNhGfftZjXqx/fnn0p57OjM5zhGuenVpxx2lzz6TarkzTrko6T45/5u1zoQ+OUDW9MkBgEyTiUvLqrbDeTEdzqOwe3lr1q9xQoNdzHmlY2ctneWUs4220fz83ueHlvE8++OzeumXl6IGl88GfaatW2ztnHv7xNt17afXRh3Pl4O/DIWcbxZ+o9u+vC3quTYb4qmeV71CwLE9HF7I8AKt9zNtXccjNRosP9uxW/vdnEpW0TqfhzdotAAz/bzpCX3fLcScu9O5CZ0bt5nhpZdGDjhmwwZp8mTp5ZelE09UrqIgClAWIQdAzsiUpWVVeefWZnAs4FjEicXOsAsfOz9T7txGajToHX3a9Ald+Nrswc9/h+0DKbcEy8rSeuXCr//0eqc6VflzPPMvnu9cfJv7ptyn/076b9Tx2SZzL+TMWDLDqbwVTXi5Xa8JYLSAYY+FN/q7rO9lUfd2eEu/zLHbHOsE1HiNBr0w4s2oxLNpg02dI2NNmyZNmRL7nGrVpIcfzumQYyiIApQi5ADIORZo7E56+Uaf2SYVHc7LW7xycajhYPmAYXs2Tup+UujcJ757wuk67iy3+t/Sq/BjypApoYvyga8PdGZHoo19+bDloeBi433i+yeijtGqUXnn2syHBZJowgNP20ZttW2LbUPLqcJDhgWRhrVL+yTZrIidH23PiNe40Fy282W6fJfLE/r+9mvXzzkS0ah2I+fIOb//Hv8cm+GZE7nJZK4J6qw1kCz25ABAljUatAv1ovVF2uXxXSpVTckubqxSVKRSvdbwzy6QPPs+va8+nB25KZ8FlvVXrw/Ngtnd49emvRb1c6/8v5WhDeZWqtcuwjw1qtUIhQwLHF+d/lUohD4w5QGNnzu+TKPB8D0etl/Eu/j/dcmvWrRiUcRZFHsf+zzIMpMmSX2j95kJ6d5d+uGHdIwoq35nBGHWGqhMNiDkAEAVNxoMn+2wi2qvipWx2Qvrx1Fhidb6Vdqk7ib67/6ly6cOef4QTf17aoVGg8aWXNnG7qpmIWft1W5VLXPUS0c5Iaf8civv9beOfysUHOxrm/bPtKh7O6wRoFfy1mZcwjufR2s0CDh7bjbfXPrjj9jL1UaOlK64Ip0jA5BmhBwAiGDOssibzL3O517XcfN/4/6vTIWs8ODStVlXPXV46SxEu/+20/zC+RE/Z/dNu+uHs0rvLm9xzxaauXRmxHNtKcmsC0qXlvR8uKfTayQSawq4eNViVdZt+96m9vntI4YXK7sLZJRHHpHOOCPyc1ZdzdpTTJ8uNcvuJagAYqO6GoCs6PURPtvhLU2yu/sfzf6oQqUr7/Utmm3hlKk1do/moOcPitiU0I7+Hfrr9WNfD33Ore/f2gkrkViFqfCQ89h3jzkzLtFmbcKFz0LYcjCvrK4FhlYNWpU5d0CXAfp71d+hZVfhh1dO1/PwQQ87y9MizYoUrClQi9taqLIGbTdo4/ck2T2yefOkoiKpbVupbumGeqBKnX66tGiRNHy4O2tjszv20vbiNG8uffABAQdAGYQcAGVYv4rygcGbxbBmeRYyvF4g1osj2syIdR33ysfaBfmOj+wYsdGgOWHbE/TsEc+GPv8Bzx4QdXy2AdwLOba23AJR+McKZ583nIUI+1oihYbuLbqXOffiPhc7HzfSTEf5MDLxtInOx33l51f0wNcPaPa/s52vccnqJU7TwLsm3RVqxhm+HC2enq17Rn3OxuBbh3MLN489Jt16qzTzf7NS9etLgwdL11yT+MXm8uVu2d/ZsyW7I3fkkVInZpEQge0jufpqt3qazer88otUp4500EHS0Ue7rwNAGJarAVnIen7M/Xdu1BkMWyJlpWnNn8v/1I2f3xg1jAzsPlCX7nxpaDlXx7s7Rv285+x4ju4bcF+o6lasmQT7uN6SLhtv3Ruj3+U/dutj9cJRLziv26+kXo/0CpXgLR8w7Gsb0nNI6H2f+fEZZx9J+OZy7/X82vlpKY3rVzNOC0/W4TzZkHPn/ncmXF2tAvuTccEF0r33uhee4X9CbNlQhw7Sl19Km5QNghU88IB02WXS6tVSjRruHXm7O28XrE884YYmAADCsFwN8KnRoB1Wucqr9GSbqyfOnxg1YBy0xUHOMiljfUAuGntRxKaE9vKa3a/R0H5DnXN/WfyLs18jmmH9hoVCjs24WD+QaHYrcD+/sWARrnzQCF/eZCV7LZxEKr1rjQm33GTL0Lm1q9fWhFMnRJxBsc8RXu3HXv/6jK8T/v6HlzD2txlnSdqbcdrs0FXjr0q6w/nJ251c+U86bpwbcEz5e2QWUqyM7+WXu0ElGpsFOuec0rfXhS3/e+01adkyd/kRVaAAAJVEyEFgxWo0aBeF1p28ZYOWobKzb05/M2qzQWseuVfHvZxzx88Zr0FvDCpzTrj7D7xfZ/c623n9p79+0qEvHBp1jLZ53As59vFs6VU0K9eWNhu0UNGkTpMK5XG9kGEb40Ofo35zXb3b1Qn197BZj78v+9sJKbEaDRr7fN7sSzwWXHZpt4uCprQZZ0nckJGKZpy+dDi3gGMzL+vXR37egs5zz0l33OFuBi9v7drYFbDs/T/6SBo/XtrL/T+X86ws8t13S2+95X7/tt1WOvdc6Zhj3NkzAEAFhByknbdC0rt7bxWsrBRutI3mA7YYoHb57ZxzP//9c6e3RqRGg3bce8C9oTDy9I9PO704onnuiOd0/LbHh2ZRrvgo+oWXbUjfS3uF9oxEq6Rl+y+8Er/evgnrRxItYOzYesfQubZP4pnDn4ncmLBm3TJ7KLpu0lVLr1ya0PfbLmiv639dQudaqLFQhMxpxplxHc6tZ0m0gOOxC/GpU6Vdd6343PvvS0vj/OxaiHrySUKOeeYZ6ZRT3E323vfd/g2++EJ6/nnp1VelmpTfBoDyCDmI2GjQO6zDd+0atZ3np/8z3ZmZKD/L4R2X9r1UbfPbOue+MPUF3T/l/oizInZ8cson2rX9rqE9FRe+f2HUcb3T+J1QyPlt6W969LtHo55rG709XtNBE75nwztstsLToUkHZwlPeKNBCxne7Miu7Uov1nq17uV0bi/fLyRSo8FtN91Wk06flND330LMid1PTOhcZMb/l3u+uqdS72tdyG12sKqa8qW1w3miMwcWVCJZuDD++9rFvFVty3W2ud4Cju1XsiN8tsuMGSNdd510/fW+DREAMhUhJ8saDUYLGHt22DO0n2Lc7HGa9MekCo0GvdcfP+RxtWrolrW9ecLNunPSnRUaDXp+POtH50LdvPzzy7rmk2uijtH2Z3ghxzqOfz7v86jnhi/xspmGTetvWmHplTeTYUu6wqtN3dD/hqgzI1s13yp07sFbHKyCoQUJNRrcvuX2Gn3YaCXCLhTDZ2CQmyxQh4eJRNksi72fzWBudAnnMPb/yMKMhaeUdjjfd193diHWbI5tBt1uu8jPxStI4AWpTVNfMCLj2dJAr0xyJDYrfs890lVXUV0MAMoh5Gwk6+kRfvf+939/L9PNvHwoOW+n80LnP/btY/r0908jluq145szvgmtnT/v3fP08LcPRx3HvIvmhQLGuzPf1R2T7oh67rI1y0IhxzbM/7Xyr4jLliw0hJfmtTvCtn+k/AyGd3j7W7zlXa0bti6z5CraBnabQUl0I7RV1rIjETYD5c1CAVXNCjpsDAshVRlyPBZo7OOm4mM7zj9fGh3jhoBdlJ91llSvdCa1jAMOkBo0kFbE+P7ZTIWVCs4VFmIKCqRatcpWlXvnnfhLA+39vv1W2nnnlA8TALJJjSA3GgwPDHax7vX3MJ/9/pnmF8yPGC4suFiJVc+wj4bp47kfR5xBseVdG67ZENqgbfs6Xvr5pajjOq3HaaHKW1Z1y/aNRGMf3ws53tIrW18fKTSEr8Hv27avhqwdEjGIlA8jZ/Q8Q0dseUSFc2x5V/m7v7aUKtHlVPa9Dv9+A0Fj1eU2hs2yZKUddnBnGGzje3gBAu/3xR57SCNGRH9/u4i3mYdhwyI/bx9zm22kAQMUeCtXSnfeKd13n/Tnn+5jFlastPbhh7t7mxIRXp0OAJA9fXKuePsKFdcqrrDsyi7GXzzqxdD5VsXKNqbbc1ZVK5wFC1u65Nnvmf30wawPIn5eCxIWXLyL/KNeOkqvTns16jhXDFsR2t9xwXsX6M0Zb5YppRseHh4Y8EAo5Lw38z39vPjniL1A7Nih1Q6hmQjbiG9Byja2V+nSEwCVYr86u9zTpdLNOGeePzO7/y9PmOBeoNtsg12Mb7WVO8szaJA7IxGL/dmxoHPzze7Mj/d9sMDUu7f09ttuF/sgs0aoFgi//77scjRvedq110pTpkhjx8aezbFQuGCB1CJ6zyoAyMU+OVkRcmStQSIsN7YSuuEVpvZ+am+NmzOuwnkWHCxYLLpsUZnZma///Lo0YPxvw7l3/Ge3/6h6NXeDre1vsSVo0faB2Diy+mIFQPY048xE9mekMr8DrbiA9dOx3joNG7qNQK0iWy78PrVAaA1RvSICkdx6a+xy2xZwjjrKrbIGADmgMGghZ8jLQ9Q4v3GF5VcNazfUMVsfEzp/1tJZzvKx8o0GY/X6AICN6ZPT5o42STfjrKo+OcjiWRwrrLB6dewAc8QR7vK+SI1V7XmbvfnqK2mzzVI6XADIxpCTFXtybtv3trhfiOnUtFNaxgMAvjXjRPazHkKxAo6xJWqffuqW3Lbmn7fdVlp+25YDHn+8dNNNUuvWaRkyAGQbpjgAoAqacVrZcwsxXvNNj/eYPf/uie9ufDNO5Bbbo3Pxxe7Svh9/dPfpLFrkNksl4ABAVIQcAKiiZpy218aKCoSzt+3xBZcsIODAZTMz0Upshy9H2333sr2D7P123FFq0iTlQwSAbJcVe3ISWXcHAJnAfqWmtBknguGCC6T7749deODzz6V+/dI5KgDIaMlkA2ZyACAFzTg3b7y585KAg4huvFHafnt3OVo4m7ExVkKagAMAlUbIAQAg3axkthUWuP56qVWr0sf79JFef10aPtzP0QFA1mO5GgAAfrLmnwUFbtU0KxkNAMiNEtIAAASWLVmjmAAAVCmWqwEAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAAAAIFEIOAAAAgEAh5AAAcsOqVdKff0qrV/s9EgBAihFyAADB9uOP0nHHSfn5UuvW7ssTT5R++cXvkQEAUoSQAwAIrk8+kXbaSXr1VWn9evexdeukl16SdtxR+vJLv0cIAEgBQg4AIJiKiqSjjnJDjRdwPPb22rXSkUdWfA4AkPUIOQCAYLLZmyVLpOLiyM9v2ODu0RkzJt0jAwCkGCEHABBMEydKNWvGPseet/MAAIFCyAEABFO1BP/E5eWleiQAgDQj5AAAgmnXXd39OLHY87vtlq4RAQDShJADAAimww6TNt1Uql498vP2ePv20v77p3tkAIAUq5HqTwAAwEaZP196/323WtrWW0t77JHYEjPbb/Pmm9Jee7nvG15FrUYNqX596fXXo4cgAEDWYiYHAJCZCgulY491Z1vOOEO64AJpzz2lLl2kzz5L7GP07i1995102mlS3bruY/XquR/PHu/RI6VfAgDAH3klJSUlylCFhYXKz89XQUGBGjVq5PdwAADpYj1sbK/M11+7pZ7LFxSw2RcLOn36JP4xrZT0qlVuyEm0KAGSY/9WNsvG9xeAz9mA30IAgMzz0kvS5MkVA44XVuzxyy5L7mPahXeDBlyAVzUr3vDAA+5SQlsGaMsE99tPGjvW75EByGHsyQEAZJ6HH3bDSLRGnvb4F19IM2e6y9fgD9vrdNBB0rhxZf9t7O0PPpBuvFH6v/9TVvr3X+mpp6RJk9zZKZtZPPFENygDyHjczgIAZJ5Zs6IHnHBz56ZjNIjGQsz48ZKtfA9f/e7NwF11VeL7pzKJFazYbDPpooukF190j7PPdh/76CO/RwcgAYQcAEDmadw4sfPy81M9EsSaxbn33thh1Jav3X23sorN3Bx5pLR6tRvcvOWR9vqKFe7M1dSpfo8SSI/iYumPP6QFCxK78ZRBCDkAgMxzwgnx9860bSv17JmuEaG86dOlZctin2Nlu8OXsmWDm25yX0aqy+QFnttvT/uwgLTvtRs1yq1uab9r27SRNt9cuu22+E2WMwQhBwCQeYYMcWdpYvWwufpqetz4KVJRiEiy6e6vzdS8807sr82C23PPZdfXBSTDQsyhh0pXXunO4oT3LLviCunww8v2Hcu1kPPJJ58oLy8v4jFlypRUfVoAQBC0aOHufWja1H3ba/7phZprr5VOP105zWYabMlYZTpBLF4sPfaYdMcdbkNUK9mdrK5d3Yaqsdi/V9++yhoFBYmFF/t+rVmTjhEB6Xf//W4D5ki/W+yxd9+VHnxQOdsnZ+3atVq6dGmZx66++mqNGzdOs2bNcsJOPPTJAYAct3Kl9MIL7t112yOxzTZuI89crqhmy8RsyYjNJtj3pEkTN/BdfLHUqlX8i/NLLpEeesi9E2shxGYtmjWT7rrLrR6WDNuYb/tyYs18vP22u48lG1hwsf1gFh5jsVlGW6qXwLUMkFVKSqTOnaU5c6LfQLGf+06dpF9/Tfv/gWSyQdqaga5bt06bbbaZzj//fCfsJIKQAwBAmE8/lfbf3w0o4ctFLKxYUJkwIXoAtD/3xx8vvfxy9NmKZ55JLugUFrqllW0jfnjQsQsf+3xWkey++7IrDJx2mvT009GX49j32sKdBU0gaP79171xkujMZ5qvzzOyGehbb72lJUuW6NRTT416TlFRkTP48AMAAEhatUo67DB3Nqb8BbgFDFs9cfTR0e++WnNVK4UcazmWzfIks6nYLjI+/9xdux9+YWRL2R59NPsCjlf22pbhRdrvZdXibAmlfZ+AIKqWRDTI8MbKaRvdY489pv32209trDpDFCNHjnTSmXe0tWoOAACkUnoWNGy8559377JGCykWfH74wS2BHIntwbGL9Fj+/lsaOza5cTVs6PbL+esv6fffpYULpV9+kQYPzr6AY2wZjvX28WbELOx4gceWS1oT2tatfR0ikDKNGrk/57H+71q46d494xvjJh1yhg4dGrWggHdMt/XCYf744w+NHTtWg+0XXgzDhg1zpp+8Y75VcQAAoKotWSJdd51bFtUuYG0fxrnnSjNnVv5jWsB4+GF3BsPWqqdiqVq8anL2vJ0Xia2xj1cRyS5sLKhURs2aUrt27r6gbAw34ewCzoKafS9vuMEtK/3ll9K33+b2fjDkhosvjn3zx260ZMFsZpxbOhVdeumlGjRoUMxzOnbsWObtJ554Qs2aNdMhhxwS8/1q167tHAAApIzdQOvXzy2N6s2K2NpyCyhPPOFWFbJ9JomyQDNwoPTVV2Uf33tvafToqrvrn0jVLwsX0c6zZVZeoYFo7MIm0UasQWffS/s5SOZnAQiCU0919/fZ70ObtfF+p3ivW6GTk09W4EJO8+bNnSNRVtfAQs7JJ5+smnaXBwAAP9nme1tSVT4M2CyHPWb9ISwAxSuPbGzWY+ed3WVk5X3yibTLLtI335SWwt4Yffq4FdVisa+hd+/Izx17rFt0IJY6daQBAyo/RgDBCPiPPebeqLn77tIbODvtJF14ofu7JAtma1O+J2f8+PGaM2eOTs/1fgYAAP/ZkjLbUxFt2ZaFHJvVefbZxD7e9de750eaHbHPYbNGdpFQFWy2qG7d6BcXNktjpV/33LPic1b9zO7KxmIf1y5gmMkBkJcnnXCCu8fPipHYYUs2jzsuKwJOWkKOFRzYeeed1a1bt1R/KgAAYhs/Pn5FIPsD/vHH8T+W9aixksux9rlY+KmqpnnWm8XCl42//N4cKyhQr55bPa38BYjNJNnsji3Di8T7fljpZCsgAADhwotvZJGUh5znnntOX9hdMwAA/GahI95dSNuXEm+DvrFqYvGaRnrnJVOWORYrIW0lm61Xjvd12FJwW4L39dfSDjtU/Fps7byNM9peHJu9eu89t2BCFl7IAECV7MkBACBr9eoVe+O9sfBg58WTaBM8K6gTr3RzMvr2lcaMcZfJ2V6gTTaJvn9o4kS3SlgsFmxsk7EFJwAIiMzu4gMAQFWySllbbBF7xsKei9G4OsSKCey+e+yPZeHGGnSmYg27LV9r3z52gYTvvov/uS30WWlkAAgQZnIApJ4th/noI/eusl1w7bqr1L9/1mxeRIDYz5xVKLNwsmZN2VkdCyv2s2pVhRKtIvqf/0j77hv9c5nLLpNvbClbvGanNs5atZSRbOy2PO+FF6SlSyVrEm4BdKut/B4ZgAxHyAGQWnYn+aijpNmzS5fs2H4Hu5v+6qtuZ2UgnXr2lKZMka691v0Z9IKOlXu++mq3bGqi7Fzby3LGGe7b3seyzfwWHF56SdpuuxR8EUmMLxH77KOMs2yZW87bQo797rAAat/X226TrLm4FXSoymWAAAIlr8Qa2WSowsJC5efnq6CgQI0SXfsMIHP89pu7EXrVqor7IOyuuf2/thBkS24AP9ieFisMYGWTN9208h/H+uo88ohbbtUuxPfYw61WlkRfuZQ5+GC3sECkvUg21oYNpXnzEt9jlA52aWIzvvb9jDRur9z1f//rx+gAZEE2IOQASB272/rUU9ErVdld2LPPrro+IgAq+ucfd3netGnu296ffbvRYEURrLS0BYpMYo1UbUlrLPb7w5q6ZkKQBJBx2YDCAwBSw0rWWk+PWKV47bnHH6/YeR5A1bHqa5MnS3fdJW25pVuooGVL6aKLpJ9/zryAY55/Pv5SNJvhee21dI0IQJZhMSuA1C0DSqSHyMqV0ooVmbVUBgiaBg2k8893j2ywZEn8Ut82E2XnAUAEzOQASA0LLYk0FrTN2bFK4ALIPW3axP/9YTPBVm0NACIg5ABIjbp1pSOOiN9D5IQT6LIOoKxBg2IvdTV2c+Tww9M1IgBZhpADIHWGDXOrN9lRnj1mIefyy/0YGYBMtv320oknxu6ldcMN7jI8AIiAkAMgdXr0kMaMKb0QsVDjbSa2bu1W1pamfgAisaIkZ53lzvR6N0Us9NSrJ91xh1tCGgCioIQ0gNSz4gJWLWniRPcixao5HXusu6QNAGL580+3aevSpVK7dtKRR7q9fQDknEL65AAAAAAIEvrkAAAAAMhZhBwAyCVTp0qnny41buyW7+7WzW0SuWqV3yMDAKDKEHIAIFe89ZZbDGL0aKmgQFq3Tvr1V+nii6Wdd3YbuAIAEACEHADIlc3bxxzjdpEP7z9i2zLtsBmes8/2c4QAAFQZQg4A5IJHHnFnbqLVmrHw8/LLbhgCACDLEXIAIBeMHSsVF8c+x4LOZ58pK9jSurffdksL25I7AADC/K8rHwAg0GwWJxHhS9ky0erV0uWXS489Jq1ZU/r47rtLDz7oFlIAAOQ8ZnIAIBf06eN2jI+nZ09ldFAbMEB64IGyAcdMmCD17cusDgDAQcgBgFxw1lmxZ2mqV5d22y2zZ0JefFH6+OPIy+5sqd3y5dIVV/gxMgBAhiHkAEAu2GoraeRI9/W8vLLP2QxPfr706KPKaDaDUy3Gny0LOrZPZ9GidI4KAJCBCDkAkCuGDnVnQ7bZpvSxmjWlE06QvvlG6tJFGW3atPjFE+z5WbPSNSIAQIai8AAA5BLrlXP00dK8edKKFVKbNu4sTjaoV09atiz+efXrp2M0AIAMxkwOAOQaW67Wvr209dbZE3CMhTPbOxRL27bSttuma0QAgAxFyAEAZIdzz3X3D5XfUxTuyivjByEAQOARcgAA2aFzZ+n116XatcsGGa809kUXSeec49vwkCIlJdInn0jHHy9tt53Ur5/03/8mtnQRQM7KKymx3x6ZqbCwUPn5+SooKFCjRo38Hg4AoLKsvPOzz0pffunOxOy6q3vRavtskrVwofTww9KYMVJRkbTDDtLZZ7u9gBAsVvb8lFOk555zw6xXBt1+hpo0kT74ILN7OwHwLRsQcgAAqfXee27Bg5UrS0tAW7ln2w/02mvSnnv6PUJkqv/7P+nmm93ZnPJsNs9+hqyaXuPGfowOQAZnA5arAQBSx0pTH3KIG3DsQtXCjR3e7M6AAdLUqX6PEpnIqv/dfXfkgGPs58iWrD35ZLpHBiALEHIAAKnjNSCNdKFqPW1s+dGoUWkfFrLAxx+74TgW+7l65ZV0jQhAFiHkAABSY9Uqt1CAt48iEnvu+edLZ3cAT7yA47EZQQAoh5ADAEiNwkJ3tiaedesSv6BF7thii/jnWDGCrbZKx2gAZBlCDgAgNaz6lZV7jqdBA/cAwvXoIXXvXlqsItpM4BlnpHNUALIEIQcAkBoWcE48sbSPTST23Gmnxb6QRW6yMtEPPOD+jET6+bDnrQz5Hnv4MToAGY6/KgCA1JYAtl444c07PfaYlQC97DI/RoZssPPObgGCbbct+3j9+tLQodJTT7lhBwDKiXF7DQCAjdSpk/TZZ9LRR0szZ5bO6tgyI9tzYZWx2rb1e5TI9KDz3Xfu8euvbsDp358ljgBiohkoACD17E+N3ZGfONF9e7fdpF135S48ACAl2YCZHABA6lmY2XNP9wAAIMXYkwMAAAAgUAg5ABCJNad85hmpTx9343x+vnTCCdJXX/k9MgAAEAchBwAiNac87DBp4EBpyhRp9Wq3seXLL7uh5+GH/R4hAACIgZADAOXdcIP0zjvu68XFpY9bRTDbQH/WWW6lJwAAkJEIOQAQrqhIuuceN8xEY/1d7r47naMCAABJIOQAQLgff5SWLYt9js3ovP9+ukYEAACSRMgBgPIBJtF9OwAAICMRcgAgXLduUq1asc+x5Wq9eqVrREBwbyg88YS0ww7u/yk7rLlfp07SxRdLv/7q9wgBZDFCDgCEa9LELRVdo0bs8tLnnpvOUQHBYjOhRxwhnXaa9P33boEPO5Yvl2bPdve8bbml9Oijfo8UQJYi5ABAebfcIrVt695ZjuTUU6UBA9I9KiA4br21tIJhpCIfXug54wzps8/SPjwA2Y+QAwDltWghTZ4sDRki1alT+rgFn7vucu8u5+X5OUIgu2dx7P9ReHn2aOxGw223pWNUAAImr6QkVp1UfxUWFio/P18FBQVqZOt0ASDdVqxwl8/YPp0ttpCqcW8I2ChTp0rbbpv4+fZ/zkq7x1pCCiAnFCaRDfiNAQCxNGggde/u9yiA4LA9bcmwGR9CDoAkcUsSAACkx7x50jPPJDcj2rKlVK9eKkcFIIC4LQIAAFLvtdek444rLSqQCAtDZ5/NHjgASSPkAACA1PrpJ+nYY92laoluBbaiA7YP7qKLUj06AAHEcjUAAJBad97pvkwm4BxzjDRhgtsgFACSxEwOAABI/VK19evjn3fvvdJmm0l9+rh7cQCgkgg5AAAgtdasSey8fv2k7bZL9WgA5ACWqwEAgNTq0iV+8YCaNaX27dM1IgABl7KQ8+uvv+rQQw/VJpts4jTr6devnz7++ONUfToAAJCpzjkn9vPWA8cKEzRunK4RAQi4lIWcgw46SOvXr9f48eP1zTffaLvttnMeW7RoUao+JQAAyESnnurus7GCApECTpMm0o03+jEyAAGVkpDzzz//aObMmRo6dKi6d++uLl266Oabb9aqVas0derUVHxKAACQqWrXlj74QDrtNKlWrdLHbQnb3ntLkydL7dr5OUIAAZOSwgPNmjVT165d9dRTT2mHHXZQ7dq19dBDD6lFixbq2bNn1PcrKipyDk9hYWEqhgcAANKtQQPp4Yelm2+WvvjCrba2/fZShw5+jwxAAKUk5OTl5emjjz7SYYcdpoYNG6patWpOwHn//ffVxKakoxg5cqRGjBiRiiEBAIBM0LSpdPDBfo8CQMAltVzNlp9ZgIl1TJ8+XSUlJTr33HOdYPP555/rq6++cgLPwQcfrD///DPqxx82bJgKCgpCx/z586viawQAAACQQ/JKLJEkaPHixVqyZEnMczp27OgEm3333VfLli1zKqt5bG/O4MGDnbCUCFuulp+f7wSe8I8DAAAAILcUJpENklqu1rx5c+eIxwoMGFumFs7eLi4uTuZTAgAAAID/1dX69u3r7L055ZRT9MMPPzg9cy6//HLNmTNHAwYMSMWnBAAAAIDUhRxrAGpFBlasWKE999xTO+64oyZMmKA333zT6ZcDAAAAABmxJyfd2JMDAAAAINlskJKZHAAAAADwCyEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAA/LVwojRgh9e4t9eghnXmm9N13fo8KALJaDb8HAABAznrnHemoo6R166QNG9zHpk6VHn5Y+s9/pOuuk/Ly/B4lAGQdZnIAAPDDjBnSEUdIRUWlAcesX+++vOEGafRo34YHANmMkAMAgB/uuUcqLpZKSiI/bzM4N90U/XkAQFSEHAAA/PDSS6WzNpFYuJk5053xAQAkhZADAIAfVq9O7LyVK1M9EgAIHEIOAAB+6Nw5flGBGjWkzTdP14gAIDAIOQAA+OGcc2I/X726W5igWbN0jQgAAoOQAwCAH04+2e2NY2GmPHusUSO38AAAIGmEHAAA/FC7tvThh27YsWVp4fr1k778UurUya/RAUBWyyspydzalIWFhcrPz1dBQYEa2R0tAACCaPFi6fPP3aag220ndevm94gAIKuzQblbRwAAIO2aN3f33wAAqgTL1QAAAAAECiEHAAAAQKAQcgAAAAAECiEHAAAAQKCkLOR8++232meffdS4cWM1a9ZMZ5xxhlasWJGqTwcAAAAAqQs5Cxcu1N57763OnTtr8uTJev/99/Xzzz9r0KBBqfh0AAAAAJDaEtJjxoxRzZo1dd9996laNTdHPfjgg+revbt+++03J/wAAAAAQNbM5BQVFalWrVqhgGPq1q3rvJwwYULM97MmP+EHAAAAAPgecvbcc08tWrRIo0aN0tq1a7Vs2TINHTrUee7PP/+M+n4jR450uph6R9u2bVMxPAAAAAABllTIsaCSl5cX85g+fbq23nprjR49Wrfffrvq1aunli1bqkOHDtp0003LzO6UN2zYMBUUFISO+fPnV8XXCAAAACCH5JWUlJQkevLixYu1ZMmSmOd07NjRWarm+euvv1S/fn0nADVq1EgvvPCCjj766IQ+ny1XsxkdCzz2vgAAAAByU2ES2SCpwgPNmzd3jmTY7I15/PHHVadOHaesNAAAAABkVXU1c++992rnnXdWgwYN9OGHH+ryyy/XzTff7PTNAQAAAICsCzlfffWVhg8f7jQA7datmx566CENHDgwVZ8OAAAAAFIbcp566qlUfWgAAAAASG8JaQAAAADwCyEHAAAAQKAQcgAAAAAECiEHAAAAQKAQcgAAAAAESsqqq1WFkpKSUHdTAAAAALmr8H+ZwMsIWRtyli9f7rxs27at30MBAAAAkCEZIT8/P+Y5eSWJRCGfFBcXa+HChWrYsKHy8vL8Hk5OpmULmPPnz1ejRo38Hg4yFD8niIefESSCnxMkgp+T3FZSUuIEnNatW6tatWrZO5Njg2/Tpo3fw8h59kuEXySIh58TxMPPCBLBzwkSwc9J7sqPM4PjofAAAAAAgEAh5AAAAAAIFEIOoqpdu7aGDx/uvASi4ecE8fAzgkTwc4JE8HOCRGV04QEAAAAASBYzOQAAAAAChZADAAAAIFAIOQAAAAAChZADAAAAIFAIOQAAAAAChZCDCj777DMdfPDBat26tfLy8vTGG2/4PSRkmJEjR6pXr15q2LChWrRoocMOO0wzZszwe1jIMA888IC6d+8e6kzet29fvffee34PCxns5ptvdv7uXHTRRX4PBRnk2muvdX4uwo9u3br5PSxkOEIOKli5cqW222473XfffX4PBRnq008/1bnnnqtJkybpww8/1Lp167Tvvvs6PzuAp02bNs5F6zfffKOvv/5ae+65pw499FD9/PPPfg8NGWjKlCl66KGHnGAMlLf11lvrzz//DB0TJkzwe0jIcDX8HgAyzwEHHOAcQDTvv/9+mbeffPJJZ0bHLmZ3220338aFzGIzwuFuvPFGZ3bHwrFdsACeFStW6MQTT9QjjzyiG264we/hIAPVqFFDLVu29HsYyCLM5ADYaAUFBc7Lpk2b+j0UZKgNGzbohRdecGb7bNkaEM5mhgcMGKC9997b76EgQ82cOdNZRt+xY0cnEM+bN8/vISHDMZMDYKMUFxc76+d32WUXbbPNNn4PBxnmp59+ckLNmjVr1KBBA73++uvaaqut/B4WMoiF32+//dZZrgZE0rt3b2fFQNeuXZ2laiNGjNCuu+6qqVOnOntDgUgIOQA2+g6s/aFhfTQisYuS77//3pnte+WVV3TKKac4e7oIOjDz58/XhRde6Oztq1Onjt/DQYYKX0Jve7Ys9LRv314vvfSSBg8e7OvYkLkIOQAq7bzzztOYMWOciny2yRwor1atWurcubPzes+ePZ279XfddZezwRywfXx///23dthhhzJLG+13yr333quioiJVr17d1zEi8zRu3FhbbLGFfvvtN7+HggxGyAGQtJKSEp1//vnO0qNPPvlEHTp08HtIyKLljXbhCpi99trLWdIY7tRTT3XKA1955ZUEHEQtVDFr1iwNHDjQ76EggxFyEPGXR/jdkTlz5jjLTWxTebt27XwdGzJnidpzzz2nN99801kPvWjRIufx/Px81a1b1+/hIUMMGzbMWWZivzeWL1/u/MxYKB47dqzfQ0OGsN8f5ffy1a9fX82aNWOPH0Iuu+wyp1qjLVFbuHChhg8f7gTg448/3u+hIYMRclCB9bPo379/6O1LLrnEeWlr6W3jH2BlgM0ee+xR5vEnnnhCgwYN8mlUyDS2DOnkk092NgpbALa19BZw9tlnH7+HBiCL/PHHH06gWbJkiZo3b65+/fo5pejtdSCavBJbdwIAAAAAAUGfHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAACBQsgBAAAAECiEHAAAAAAKkv8HMc7CsLUW+aIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "\n",
    "def change_c(c):\n",
    "    linear_svc.C = c\n",
    "    linear_svc.fit(x, y)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.scatter(x[:, 0], x[:, 1], c=y, s=40, cmap=\"bwr\")\n",
    "    svc_plot(linear_svc)\n",
    "\n",
    "interact(change_c, c=[1, 10000, 1000000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aeb14ef",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "##  17.11.  非线性分类支持向量机  # \n",
    "\n",
    "上面的内容中，我们假设样本是线性可分或不严格线性可分，然后通过建立两种情况下的支持向量机实现样本分类。然而，线性可分的样本往往只是理想情况，现实中的原始样本大多数情况下是线性不可分。此时，还能用支持向量机吗？ \n",
    "\n",
    "还记得之前对处理偶问题提到的核函数吗？其实，对于线性不可分的数据集，我们也可以通过支持向量机去完成分类。但是，这里需要增加一个技巧把线性不可分数据转换为线性可分数据之后，再完成分类。 \n",
    "\n",
    "我们把这种数据转换的技巧称作「核技巧」，实现数据转换的函数称之为「核函数」。 \n",
    "\n",
    "Note \n",
    "\n",
    "核技巧是一种数学方法，本实验仅针对于其在支持向量机中的应用场景进行讲解。 \n",
    "\n",
    "##  17.12.  核技巧与核函数  # \n",
    "\n",
    "根据上面的介绍，我们提到一个思路就是核技巧，即先把线性不可分数据转换为线性可分数据，然后再使用支持向量机去完成分类。那么，具体是怎样操作呢？ \n",
    "\n",
    "> 核技巧的关键在于空间映射，即将低维数据映射到高维空间中，使得数据集在高维空间能被线性可分。 \n",
    "\n",
    "上面这句话不太好理解，我们通过一个比喻来介绍： \n",
    "\n",
    "[ ![https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711018317.jpg](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711018317.jpg) ](https://cdn.aibydoing.com/aibydoing/images/document-uid214893labid6671timestamp1531711018317.jpg)\n",
    "\n",
    "如上图所示，假设我们在二维空间中有蓝色和红色代表的两类数据点，很明显无法使用一条直线把这两类数据分开。此时，如果我们使用核技巧将其映射到三维空间中，就变成了可以被平面线性可分的状态。 \n",
    "\n",
    "对于「映射」过程，我们还可以这样理解：分布在二维桌面上的红蓝小球无法被线性分开，此时将手掌拍向桌面（好疼），小球在力的作用下跳跃到三维空间中，这也就是一个直观的映射过程。 \n",
    "\n",
    "同时，「映射」的过程也就是通过核函数转换的过程。这里需要补充说明一点，那就是将数据点从低维度空间转换到高维度空间的方法有很多，但往往涉及到庞大的计算量，而数学家们从中发现了几种特殊的函数，这类函数能大大降低计算的复杂度，于是被命名为「核函数」。也就是说，核技巧是一种特殊的「映射」技巧，而核函数是核技巧的实现方法。 \n",
    "\n",
    "下面，我们就认识几种常见的核函数： \n",
    "\n",
    "线性核函数： \n",
    "\n",
    "$$ k\\left ( x_i, x_j \\right )=x_i*x_j \\tag{21} $$ \n",
    "\n",
    "多项式核函数： \n",
    "\n",
    "$$ k\\left ( x_i, x_j \\right )=\\left ( x_i*x_j \\right )^d, d \\geq 1\\tag{22} $$ \n",
    "\n",
    "高斯径向基核函数： \n",
    "\n",
    "$$ k\\left(x_{i}, x_{j}\\right)=\\exp \\left(-\\frac{\\left\\|\\mathbf{x}_{\\mathrm{i}}-\\mathbf{x}_{\\mathrm{j}}\\right\\|_{2}^{2}}{2 \\sigma^{2}}\\right)=\\exp \\left(-\\gamma *\\left\\|x_{i}-x_{j}\\right\\|_{2}^{2}\\right), \\gamma>0 \\tag{23} $$ \n",
    "\n",
    "Sigmoid 核函数： \n",
    "\n",
    "$$ k\\left(x_{i}, x_{j}\\right)=\\tanh \\left(\\beta * x_{i} x_{j}+\\theta\\right), \\beta>0, \\theta<0 \\tag{24} $$ \n",
    "\n",
    "这 4 个核函数也就分别对应着上文介绍 scikit-learn 中 SVC 方法中 ` kernel  ` 参数的 linear, poly, rbf, sigmoid 等 4 种不同取值。 \n",
    "\n",
    "此外，核函数还可以通过函数组合得到，例如： \n",
    "\n",
    "若  $k_1$  和  $k_2$  是核函数，那么对于任意正数  $\\lambda_1,\\lambda_2$  ，其线性组合： \n",
    "\n",
    "$$ \\lambda_1 k_1+\\lambda_2 k_2 \\tag{25} $$ \n",
    "\n",
    "##  17.13.  引入核函数的间隔表示及求解  # \n",
    "\n",
    "我们通过直接引入核函数  $k(x_i,x_j)$  ，而不需要显式的定义高维特征空间和映射函数，就可以利用解线性分类问题的方法来求解非线性分类问题的支持向量机。引入核函数以后，对偶问题就变为： \n",
    "\n",
    "$$ \\max\\limits_{\\lambda } \\sum\\limits_{i=1}^{N}\\lambda _i - \\frac{1}{2}\\sum\\limits_{i=1}^{N}\\sum\\limits_{j=1}^{N}\\lambda_i\\lambda_jy_iy_jk(x_i*x_j) \\tag{26} $$ \n",
    "\n",
    "$$ s.t. \\sum\\limits_{i=1}^{N}\\lambda_iy_i=0 $$ \n",
    "\n",
    "$$ 0 \\leq \\lambda_i \\leq C ,i=1,2,...,N $$ \n",
    "\n",
    "上面公式中可以看到引入惩罚系数后与原目标函数形式相同，唯一不同就在于  $\\lambda_{i}$  的范围。 \n",
    "\n",
    "同样，通过前面的对偶问题求解得出最优解  $\\lambda^{*}=\\left(\\lambda_{1}^{*}, \\lambda_{2}^{*}, \\ldots, \\lambda_{N}^{*}\\right)$  后，基于此我们可以求得最优解  $w^{*}$  ,  $b^{*}$  ，由此得到分离超平面： \n",
    "\n",
    "$$ w^{*}x+b^{*}=0 \\tag{27} $$ \n",
    "\n",
    "使用符号函数求得正负类之间的分类决策函数为： \n",
    "\n",
    "$$ f(x)=sign(w^{*}x+b^{*}) \\tag{28} $$ \n",
    "\n",
    "##  17.14.  非线性支持向量机分类实现  # \n",
    "\n",
    "同样，我们使用 scikit-learn 中提供的 SVC 类来构建非线性支持向量机模型，并绘制决策边界。 \n",
    "\n",
    "首先，实验需要生成一组示例数据。上面我们使用了 ` make_blobs  ` 生成一组线性可分数据，这里使用 ` make_circles  ` 生成一组线性不可分数据。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ab12bad",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e0a4393f90>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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/P8yavPlm9hlDhFy+OHPOGQrRA6XAr/nLLqs/UI34az6G9wcgHsEqkEcOUFdaSVp++bAEuOqqYSf23XcXe2Tp5SX+uK5HPrHTwQilwq1h3eo2zoQJs+ZpA5gVwSqQJw89JG22mfTee7POHO66q3TFFcUaWbrtsEPoK5+Nuy9tt11SIwKaxisBuaI0GxCPYBXI02aK/fYLS3p1l/Wiz/v3l77/vijDS7UNNwyz0ZlO2u68tP32oeQPUAq8EXC11bLnYvtrbtTAigEQj2AVyNOs6s8/Z88/8+zgTTclOarS4BSAxx8P6RIWtQWN/u9d0/zdUGoGDIjPWfUxAOIRrAJ54A1VcUt/nkn54IOkRlRaXHvy7bdDbq9TKVZfPSz7P/GE9NxzzD6h9OyyS1hNqX3hVfvjo46S+vUrztiAUkO2DJAHLVtmn0WpfRzq564+O+8cbkA5rBgMGSL16SNdckkoVeWVF68UOFDdYov4jYUAApoCAHngnuvOu4zz6KPSllsmMSIAANKLpgBAwtxvff31s28S8gYhL3EDAIDcEawCeeJ8y2iTUO1dwF7q69hRevLJmXPXAABAPIJVIE8WXFAaPVq67rpQtmbhhcOMq/PWvAGL0ksAADQcOasAAABIFDmrAAAAKAsEqwAAAEgtglUAAACkFsEqAAAAUotgFQAAAKlFsAoAAIDUIlgFAABAahGsAgAAILUIVgEAAJBaBKsAAABIrTmKPQAgbdyAeMQI6e23pTnnlDbeWFp66WKPCgCAykSwCtTy2mvSHntIn30mzTZbCFx969NHuu02qUOHYo8QQJp99JH01FPSH39IK64YLnb9XgKg8QhWgf955x1pww2lGTPC53//XfM1z7Suu640erTUpk3Rhgggpb7/Xtp9d2n48BCcNmsm/fWXtOii0i23SOutV+wRAqWLYBX4nxNPDLMhtYPUiE86n34qXX+91L9/MUYHIK2mTQvBqN8jrPZ7yMSJYXZ1wABp7rmljh2lHXeU5puvaMMFSk6zqiovcpaPKVOmqG3btpo8ebLmmWeeYg8HJeKbb6ROncKSfyaeKeneXRo7NsmRAUi7oUOlI4/M/v5hc8wRLnybN5dOOEE65RRSBFC5pjQgXuNlAkj66qv4E42/PmFCUiMCUCquuSa34/7v/8L7iFONTj89BKsA4hGsApLmnTe349q2LfRIAJSa8ePjL3brc/75YVWnWP78U7rrLmmDDUJu7QorSOeeG/JvgTQhWAUkLbFE2LmbbUlu9tmlPfdMclQASkFj80+d23rHHSqKqVNDkNqvn/TSS2HVaMwY6aSTpB49Quk+IC0IVoH/8bJcfZurokC1VSvp8MOTHhWAtHO5O79HNJT/TbFSiw4+OJTqM+fRRvweOGWK1Ldv2DgGpAHBKvA/22wjXXllOIFEpWeimVYv/z/9dFgqA4DaDj1U8v6QhgasDgznn19FydH38n/tILU23+9UAB8DpAHBKlDLIYeE/LPTTpO23TaUmLn22nDfGmsUe3QA0mihhaRnnqkJPHMNWh0U7rKLEuemBZlWkSK+WH/00aRGBGRHnVWgDpewOvnkYo8CQClZeWXpyy+le++Vhg0LNZvff1/65JP6A0Ov2jhQ7dYt+bH+/nsIRrNtCvPXfvstyVEBmRGsAgCQBy1bhk2Y0UZM53w6IH388VBj1UGrg1SXsPLKjZuMFMMyy8RXL/Ds8HLLJTUiIDuCVSAhX38dZl7crtUnC89sAChf7lj12GPSG29It94qffdd6GC1115hJrZY3G3LFVC++CJzOoBTFA46KOmRAfUjWAUK7MMPpeOPD7Mr0WyGTxRONdh772KPDkChrbZauKWFL5RvuEHq0yd8Xl/A6vbTSy2V+NCAerHBCiigd9+VevWSnnxy5mW3zz+X9tlHOuOMYo4OQKVad13phRfC+1PdnH23jz3zzGKNDJhVs6qqxvTdKI9es0ChrbJKCFgzlYiJZl6XXjrJUQFAjY8/DikBLtG3+uqNqxkLFDJeIw2gAvny5PnnQ+eUH3+UFl44LEevumqxR1Ze3nor3LLxpourr5YuvjipUQHAzNyxyjcgrQhWK8zPP4fi926v50DJM36+ir788lBT9LbbpBYtij3K8uAZ1TjeFfzmm0mMBgCA0kTOaoXNqG6/vfTqqzWBku/z/+2BB6TDDivqEMtK8+a5HcfFAQAAmRGsVpCRI8Pyf6b8Se8IvfHG4vWqLjcbbBCf++VduZtvntSIAAAoPQSrFcR9nr30Hxc83XdfUiMqb95Vu/POmQNWFwd3HUZXBQAAAPUjWK0gv/ySW9eSn35KakTl76qraop/Ozit/Xf28v8jj0jt2hVteAAApB7BagXp3Dm+a5LzVxdbLKkRlT9X43jxRenaa6WePUNpGFdfOProULLKqQIAkA9O4Ro0KLzX+71nhRXC5lm3fQVKGXVWK8inn0rdu8f3tv7mmxBUAQBKw2uvSZtsIv32W82+hGhywu2dvV+hffuiDhFodLzGzGoF6dYt7PbPNrvqriUEqgBQOjxzusUWMweq5qko38aOJTcepY1gtcJceql03HE1ZZWizT/e6DNkiDRwYFGHBwBoIDd48V6DTJVefP/jj0vjxiU9MiA/aApQYRycnndeCFgffFD64YeQQ7nddiFgBQCUlqeeCitm2ZL6/PWnn5aWXDLJkQH5QbBaobwDff/9VdncYuqSS6THHpP+/FNacUXp8MOlHXaYees+AKTYH3/EV3pxsOq3OaAUcUZGZbr55lBT6tZbpe+/D3W9Xn45FEb1LWrrhfLhM7Uf99VXD1ulO3QISdwffVTskQFN4kojcQ1I3PTF1+NAKaIaACrPe+9JK60U3r0zTUGccYZ00klJjwyFMn16aBXmLdGeNY8e+6hLxr33SttuW9QhAo01frzUpUvmtzQHsl27Sh9/HF++EEgK1QCAuF1m2Zb5ff3m9ACvraE8HH98KHhrtc/onkH37hPPpn/5ZdGGBzTFoouGDbJWNxh1oOoNtbfcQqCK0kWwisrjbbFxy/zeeeYZWJS+yZNDV4ZM006+OPHXrr466ZEBeXPUUdI994SaqhEHpxtvLL36qrTGGsUcHdA0BKuoPLnOmDKzWh58pv799+zHeHbVG+2AErbTTtKYMaGuqpsETJwoPflkyGkFShnVAFB5vLFqxIjMRQltzjmlpZZKclQolFwvOtgqjTLg2dQePYo9CiC/mFlF5XF5qmyBqjfd7LprqO+F0ucG6XH8mK+yShKjAQA0EMEqKs/WW0u77Vb/bgMHLQstJJ1/fjFGhkLwNum+fbPX9nEOs8tYIfj2W+n008MWcvdfdiKkd/A4/xcAEkawisrjSgDeGutWXh071tzvLbN77CGNGhUCVpSPoUOleefNHLAefbTUu3fSo0qn99+Xll02lG/74gvXlwlJkMccE1Jovvqq2CMEUGGos4rK5hk1n5yd19i9ewhoUJ4ceA0aJN1/f001iMUWC2WtDjmEuj5R3q5nU7/+uv5UGa88rLqqNHJkMUYHoELjNTZYobL55MtW2cpJB7jrrlCWzIHrXHOF5W1a69Z4+OGwhTwTB/neZj56dAhaASABBKuonkR5+unQ5Merf2uvzSQTylj79uGGWT31VLiAy1aH2KkUPo5gFUBCCFYr2NSp0qGHSnfeOfOKn1fDb7hBWmutYo4OQCrLfHkmmhrEABLE+lcFp6ZtttmsgaqNGydtuGHYZwSggjglJlOnr9pvHqTOAEgQwWqF8h6Tl1+ufw+Fz1VeBRw4sBgjA1A0e+8d0gCyzap26CBttVWSowJQ4QhWK5TboGcrO+mA9ZVXpE8+SXJUAIrKjTCuuSZ8XHfjmT+Pyr5lC2gBIM8IViuUl/qzNXGKeNM0gAqbXX30UWnFFWe+f511pBdekDbZpFgjA1ChuDyuUG5Kk61CTYRStUAF2nLLcPv0U+nHH6VOnaRFFy32qABUKGZWK1S/fvHlJX1+Wn31pEYEIHW6dZPWWINAFTPxfgefQzp3Dk+NffeV3nor8/E//yz95z9Snz6hNOIRR0hjxiQ5YpQ6OlhVqO++k3r0kH79NXM6gDtU0i4dABA5+WTprLNmLscbfeyAtH//mY9/6SVpiy1CqcQo2oiO//e/w/eirndlmtKAeI2Z1Qq14ILS8OGhu6jfKKI3i2jfhLtSugYrAAB2990huLTafSOij48+Wnr22Zr7J0wIJRKnTasJVGsff8450rXXJjJ0lDiC1QrmBjT//a905ZXS5puH2qqHHy598EF4E+FqFwAQGTw4e/qYK8xcdFHN5z63/P579tK9Z58dX9oXIA0AAABk9cMP0gIL5N7gzIHrEktIn38e/2+c77rSSnkZJkoIaQAAACBvZszI7bioqYx5T0Qucj0OlYvSVWXC8+Nuj+p63h9/HEpT7bBD2LHZqlWxRwegLN5kRo4M27hbtJA23lhaeOFijwoJ7nPwHodffsl+3OKLh6eHLblkqHyWbZnf6WZduuR3rCg/zKyWAV/F7rOPtOaa0q23Sq++Kg0bJh1wQKg889FHxR4hgJLmK+Fll5XWWks65JBQq8g1i3w1PGVKsUeHBMw5p3Twwdk7Hzrw9L6HiJ8q2QJVfy/3mHAJLCAbclbLgMt/nHfezLsta78Z+IrYtb3nnrsYowNQ0pxQ6CD1zz9nrXPnNxjv1HRnq2g6DWXLs6qeFPH5JNNTYcQIaa65wn3OXfXG3ddeq/94P2U8Wb/CCsn9DkgPclZLlOvQ3XNP2EHpbod+oeeS63PJJfUHquY3iK+/lu64I+/DBVAJjj22/kDVfJ9nXV3TCGXPaQCvvCLttZfUvHnN/Q5OXerwmWdqAlXzMV7l22OPmrKIkeWWCzVYCVSRC2ZWU8DLJC7f4dnR336rub99e+mCC8ISfyYPPihtv3327++lGXcOefrp/I0ZQAVwbbu4hEJv/+7VK+QfoWK4K9W774bzy8orS23aZD/+22+l554LG7UcqHoWFpVtSgPiNTZYpcBxx81cm652qRCnhjkn1fmnjd1F6cuRyZObPk4AFRis5nK1/dlnSYwGKTLffNL66+d+fIcOIcUZaAzSAIrM7/H1Baq1DRgw84xrba5jF8fLL927N258AArAV5Cvvy49/HCYkUxrVfRcV6dKZBULQGkiWC2yG27Ivrsymj29//76v9a7dygPkq3blGdmDzywaeMEkIWDTQefTzwR1kazZVc99FC4evTS+bbbhs1LXmq/7TalTs+e0mKLZT/Gb2C77ZbUiFBB3n9f+te/wiatLbcMpRm9twOVh2C1yHLp7uGSIV98Uf/XHKRecUVIG6svYPV9u+wirbNO08cKoB4OMr3E4eBziy1CgOddI95ZUtftt0vbbTfrsvn48dKee0qXX65U8RvLySdnD1RdZsQ1jYA88bXeCSdIyy8fNhy7woCvA10Ky9d1LlCBykKwWmQu3p9tVjTacJttlc21uf1CrrsPomXLkELg2qtxPwNAI1x6aQgy6+Z2fvCBtPnm0gMP1NznXB5vmbZMM69+wbqKeprst5906qnh42gZyG8ovvmN6amnpE6dijpElJfLLpPOPz98HHXD8kvGN2/s8oZh7+lA5aAaQJG51IeDzWx8TvDMatxqnB9JlwIZN05q3Vrq2zcEwwAKwNubF1mk5mxa3wvXtX5cO84FJW++OXtpj2gm88ILpaOPVuqMHRvWYZ3m4CthzyK7JlEJvM+idLhKml9W332X/WVyzjnS8ccnOTLkG9UASohzcVz247336j/n+UXpHZRxgWp0blx33XADUGA33ZR9Y1Q0DeQcVefiuJWcc3p8Ns7EL/i0tpxbailpyJBijwJlzmV7swWq5pfdnXcSrFYS0gCKzOcmL+EvvfTMq2xRAWXPjl57bfHGByADL/XH5dc4OP3ww/Bxq1bZN15FaldVBypMrmUW3U0LlYOZ1RRw/TknjD/2WOg09f33YSbVNVY9SxqdD70nw6twb7wROoM4fcDHtGtX7N8AqEAOKuOCVU8BecncvJ05yv3MxMsrW2+dvzECJaZr1/hjPKnjKjioHOSsloj//EcaODDMxEZdD32e9GSNVxmdcA4gQe6JnEtg6Rwfb2s2V1F3v8r6cn68nOIllqgtEFCh1lgjTMpky7K5915pxx2THBWKGa+RBlAC7rsvbBL2ZUXt9tz+fPp0aautpE8/LeYIgQrk3f7dus3a9Dzi+zfaqCZQjc6wzv00X3nW3lm/6KJheYVAFRXOkzOePY1eIrX5vvXWCyWKUTkIVlPOAemZZ2Y+f/nK05M0LvUBIEE+mz75pNSxY03AadEZ1rOkd901879ZYIEwZeTKAC5+7Pqsa64pXXVVmIF1wApUOL8knn121g6NfsnttZf0+OOZrxFRnkgDSDmXrMolh6d9+5DrCiBhU6aE4NM3vwg7d5YOOEDaddeafFUADebo5OWXwx5Fv5S84djXhqi8eC2RYHXo0KG64IIL9M0332jFFVfUZZddptVXX73eY2+66Sbt611DtbRo0UK///57RQarTl9zQ5w4fiE7JaCh3bNGjgwTQr6SrdtUAAAAoOxzVu+++24NGDBAp556qt56663qYLVv3776LkshNQ/666+//uf25ZdfqlJ5kiYqZ5WJg83FF8/9e371VUi3825K1/Teffew3OIa365fDiDPnGzuDVmuCOA811VWkQYPTl+3KgBIoYIHq0OGDNGBBx5YPVu6zDLL6KqrrlKrVq10ww03ZPw3zZo1U8eOHf+5dXBtpwrlslRuJR4XsLpnci68SulZ1OHDZy756I+fflrq3Zs2dkBeeVXIuyBdOWDYsNBizrXqBg0KgeubbxZ7hABQucHqH3/8oTfffFN9atVVmm222ao/H+n15wymTp2qxRZbTJ07d9Y222yjD1x8O4MZM2ZUTyXXvpWbs86S5p67/oDV9y2zjLT//rl9L0/mTJpUf+Uc3zdhAk1qgLxyKY+nngof1y7n4d2Rfr9yIl4Zvm8BQEkEqz/88IP++uuvWWZG/bnzV+vTo0eP6lnXhx9+WLfddpv+/vtv9e7dWxMnTqz3+HPPPbc65yG6OcAtNz16hCTzFVaYdfnfEzYvvCC1bh3/fRyMuhtW7fNlXf6aNyZnq28HIEde5r/uuswvKL/gfvpJuvXWpEcGACUjdaWr1lxzTe21117q2bOn1ltvPT3wwANaYIEFdPXVV9d7/KBBg6qTc6PbBE8NliGXavTK4ejRoYuVsyhcKeDBB6X558+9PV0urezczpyJHiAPnnlG+vPP+OPc2QMAUK+CVipr3769Zp99dn377bcz3e/PnYuaiznnnFMrrbSSxjnPqx6uFOBbpfC+DN8aw92uPBsbV//BZSJpTw7kwW+/xR/jF+S0aUmMBgBKUkFnVps3b65VVllFz7q67/94Wd+fewY1F04jGDNmjBZaaKECjrQyOFjdZJPsm7X8NVcKqKD4HygcNwaI4+rmyy2XxGgAoCQVPA3AZauuvfZa3Xzzzfroo4906KGHatq0af/UUvWSv5fyI2eccYaefvppff7559WlrvbYY4/q0lUHuMg2muyEE7Lno/prxx2X5IiAMtarVwhY6+sbWTuZ/KCDkhwVAJSUgjcs22WXXfT999/rlFNOqd5U5VzUYcOG/bPpavz48dUVAiI///xzdakrHzvffPNVz8y++uqr1WWv0HTrrx/2exx4YEgJiDZbRbOt118fukACyAO/yLyrccMNQ1Ba35XiYYdJq65ajNEBQEmg3WqF+u9/Je9Ze/HFmiDWkzuLLVbskaFsedeed727fIXfdjzr6BWWXHcIljKX6jvqKOmNN2Yuonz88dIxx2SfeS1Hf/wRgncnxzugB1BxpqSt3WqSCFaBFHIXiu23DxuJol1+/v+cc0q33CLtvLMqgmtGf/aZ1KaNtNZaTuxXRXEXr4suChcs5h7PRxwhHX44ifJAhZlCsEqwCqQqQHMJC5dwqrsM7oDVt+efJ/+k3J1xhnTqqSHnKMo/ih5/B+5unEAZEqBiTGlAvFZha09Ain33XcjLGDXKrdlUNi68MAQn9eVrRjOs55xTjJEhKZ5JdaBqtbuS+PH38+KVV6TTTiva8ACkG8EqUGxuZLHLLlKnTtJ660lrrBE+PvPM+vvilhIHI3fdlf33cPDiWTV3rUB5uuSSUKIrEwesbp03fXqSowJQIghWgWIHqquvLj3wwMwzTm7B6Zmofv1Ku/etN9L8/ntuQW0u7dVQujOrcRde3oD34YdJjQhACSFYBYrJRW1/+KH+E7kDuPvukx5+WCXLG4i86z2X4xZYIIkRoRhy3RpRXlsoAOQJwSpQLA5SHYxmm3HyZpTLL1fJcj6qa6Jla5vm5eHddgst1lCevIEq23PA5p47t45fACoOwSpQLB9/HL806tSAd99VSevfX1pwwfpzFh3AOEg56aRijAxJOfLImdNc6nseuEuhnwtp41JjLkp92WXSyy8z+wsUAcEqUCy51pUs9fqT7lbn3d4rrxw+dwH8qAj+UktJL70kLbHEzP/G1RBuuCHk8zqNwN0qnDLx5ZfJj7+UTZ0qDR0aSod5095KK0mXXhryQ5O08cbSwIHh47oNEPz5iiuGDYVpW/nYaitpySWlQw8NF10ur+ZuirWbOwAoOOqsAsXiuqMOIHxSzMSzke6Ne8UVKgtvvhnKc3nTmKse9O49awejX38NwY1LeDmQiTaYefbNgfvjj4eWa8hu4sRQXeKLL2YuE2aLLho2PSXZss4//847pQsukN55J9znGXe3m3UXrzTNqrp5hS+UvPpRd0bYz0PnWL/2mrTCCsUaIVDyaApAsIpS4fqiXgLP9DL0idFpAMsuq6L7+efQgciVCjp3lrbcsjCzvnvsEcpd1bds7ODVua2eYc1l41al8vNptdXCc6e+VBNfBHlW+733itPu1M8hV4rwprq4XNZi8JK/2+Nme11uuqn02GNJjwwoGzQFAEqFe8Pvumv4uPZJ28GEP7/ttuIHqg4aTzhBWmghae+9w3LujjuGz6+/Pr8/6+uvMweq5llWz3rddFN+f265efXVMIudKSfa97//fugcVgy+0OjYMZ2BqjlHNRs/P594Qvrmm6RGBFQ0glWgmKKA9JFHpI02CsuinrU8+GBpzJiaQLaYvEw7eHBNV61oWd4zrd4Uc801+ftZzz2XfSOOebbLqQDIzH+fbEX4zV/n71g/z9zHLTr6666TDKDgYt7NABScl7a9kcO3tHGR9rhg9NhjpT33zE9fdy8N56Kc2tEWgv8+ccv7/nouDRsqkZckvTktTtu2SYwGqHjMrALIzDvy42bovLP8wQfz8/O8KzyX2eiosgDq59QRb+DLxqkAyy2X1IhKy+67Z09RcKDvv123bkmOCqhYBKsAMvvvf+OX5R3M5quklINQl1fKFih4PE6TQGa77CK1bp19drVly9CMAbM6/PDw96lbZqt2CsAppxRncxpQgQhWAWQ277zxm2AcPPq4fM7mOlCo+3OjwMBBQrE3naWdy0Bde234uG5AFX1+1VVhuRuzckmvp58Ofx//vaK/mZ+TDmAvvljaaadijxKoGASrADLbeef4Lls+eW+7bf5+Zs+eoYalSwPVDrS6dAnVB04/PX8/q5x5c55LjdUN7F2y6qGHpL32KtbISoNrAI8fL115ZXh+b7GFNGhQqFvrslYAEkOdVQCZeee/63W6Hmd9QasDVVcEiCv101iTJoVUhDZtQo4gy64N57d4l6n69ttQbWL55fk7Aig6mgIQrAL54yDHs5zuOuRlUC/7O0/VwatnXm+5pfRbwgIAUhuvUboKQHYdOkijR0tPPRXaZf74Y8jp22+/MOuKwvrsM+nuu8PffZFFwqYoPyalaPJkadiw8P+uXaUNNkhvYwAAqcHMKgCkkWugOsXi9ttrNvZ4Vtv/d+ezM89s2nL+9OkhCPb3/+67cAGy//6hjW6+A0jPwrut8CWXzFzb1cH35ZdL22yT358HIPVIAyBYBVDqvNv8gQdqOobVdeqp0mmnNe57e+PQhhuGWVsHv/4ZUYrH2muHzlb5fP/cd1/p5ptn7QoVBdv+PfO5SQ9A6hGsEqwCKGVvvx3f+MB5wl9/Lc03X8O+twPSFVaQPvmk/k1zDlo903n//cqLN96QVl8989cdsHbqFGr1khKABPkarXZlMqQ3XqN0FQCkjTetxXUOc2va++5r+Pd27rHb6GYqSeZg1jOdnnXNB5cby/a7eL7kq6+kZ57Jz88Dsvj1V2nw4FAJz9dGrVqFNPA33yz2yJANwSoARNwPfuhQaZVVwmyfu2ldemloKZskz5hmWv6POAD0cQ3l1rhxgbBTA1yLNR8+/TS+Vq+ntj7/XHnnXFynS3TuLDVvHh7Tf/87lERDxfnpJ2nNNUO5XFfEM6dQ33uv1KuXdM89xR4hMiFYBQCbODE0JDjyyLAM70Dw3Xel/v1DbdLo7JYE10PN1Ooz4gDQxzXUtGmz5o7W5Z/t4/LB3c3ifhePx7V088lBstMdzj47PLZ//hkeU0+r+fEcMya/Pw8l0UV37NhZrwP9UvJ9e+whTZhQrNEhG4JVAHCw5A0+zpv0x1EwF33smbittooP8vLFZ8242UjPju64Y8O/d7du8cf4Z3fvrrzwGONmiZ1/6w5R+X48f/ghpDXU5s9dOsuPZ92voWz5JewZ1EwPuZ8yfpoWqr8JmoZgFUBh+Szg9qmHHhoCiAMPlF54IbnALxevvhqS1jIFiL7fXaBGjEhmPK5f6+At04YjL5sPGCC1b9/w7+3yVHHBo2dD87U7f/vtQ4JgptQD/y6HHNLwjWLZPP98yMvNFJn4fl+YuOoB8s4T2CefHLIv5porPPye4Hap4GJ55ZX4axN/ffjwpEaEhiBYBVA4v/0mbb11SBS77jrp4Yelm26S1l9f2mij5HNBM3HQEpfH6a8nFdw4gHMNVM/+RT97zjlD8Oqv/etf4ezfGIsuKp1ySvZjnLfbsqXywrOm3jzlmqoWpQREf293QbvgAuWVf17c4+m/J5FJ3vmazlkW554bsi+cE+oMGj/lVlyxMKnJuch1Ep3J9nSigxWAwnGXqyeeCB9Hs5bR/198MQQq7mhUbDNmxNev8ddrF7QvtLnnDpuhnFvpzmHeHeKAb889QwH/pvCmo3btpDPOmHm6a/HFpQsvlHbYQXnlblVOFvQ6rHex/PKLtOSSoemBL2TyXTvI+am5fM+4VAs0iP+cXhDww1s36PNkvjs3b7dd6NycdLmoVVeNP8bXN717JzEaNBTBKoDC8AYXzw5m4rOZyyh5+d2774tp2WVDgBN3Jl5uOSXO01S+5VM0O+vld6c2OGD1mu1aa8VvhmrKDKtzcX0rND+f4h5Pfz2XCAY5e+yx0G8i20vovfekl16S1l03yZGFa6M+fUKGSLZsH2crIX1IAwDQcN648tZb0rhxmXNPPYsWV+TdUxnZAtqk7LKL1Lp19ukeL4vvvnuSowpndW9QcsFsJ/+5u5T/XnE5p7lyOae+fUOhyXXWKVygmjRP380/f+bfx4+zqw/sumvSIytrzqrIJZumWCV1nYnkp0XdMUZPEy8qLL10UYaGGGXyzgQgER9/HIKnDh3C7JV3lnvWr76A8+efcwt+vGZYbF5yv/ba8HHdgDX6/Mor89uCNM5FF4XpJ+f5upK5UxBGjgwBloNmkuuyB+FOnfDFUt0LJn/u56UbL/hxR97ETWZHL6dcjisEZ894Icd7DGunZPutzBk3AwcWZ1yIR7AKIDfOnXTbTAdPtWf2vOvaAVTdTTI+M8QFVJ6V9YafNPDv4HVMpwTUttRSoUD+3nsnO6N6zDHh49prltHf3RcHF1+c3HhK0cYbh7+j135rX4D4AuC55/JX7QD/cIfguJe8A1X32iiWhReWrroqZL54Yeibb6TXX+fpkHbNqqrSVD8m2V6zABrA0w8ukp/pbOSAwDOvUR1Pnw0WWij7NIr/jUsIOV8yLfyW+MEHof6OZ5A9c5z0bhBvcHrkkewbgHzW9d8uLtUCYWePbwssEJ6TKAgX9/Cfd/r0+rODPKHtfX3uruvJb1S2KQ2I15hZBRDP+am+ZZs28ZmodkVtJ4d513k2nj1MU6BqDky9kcozc+6AlHSgak8/Hb9T3Wd8Tw0hni86/FgSqBaU4w1XprO6GUBR9sXttxOoouEIVgHEc/vROA5kR4+e+T73YXd7yyg3MJoFdMKYCy+ed14BBlsGci2pVKzkPyCDnXYKG61ckay2DTYI1eo22aRYI0Mpo3QVgHguoJ6LulMmnpU89thQD8Z5n15a91Ksd2u3bVuQoZYFJ/WNGpV917+rF7h+KZAy7vfh24QJ0vffSx07Sp06FXtUKGUEqwDiueOUA89sKe5e48s0beLAKon6muXiyCPDzv9MPEPtLc2tWiU5KuSbX0/e3eNccF/oeTNY1OmrDDjDJ21ZPihNpAGgQe+r7p5J05cK5B377vGerVe9l/b33TfpkZUn1311ibD68mX9GHTvLp12WjFGhnxxDSX3H11jDengg8NrxxU03NUtDeXcgBQhWEWsyZNDV0Yv4zj10I1ottlGevnlYo8MiXIdUpd1cgBVO4hyhW0/KVzSypuq0HSepb7rrpDTW3tTkF+Ahx8uvfqqNO+8xRwhmsJtnFxCy2XfanPaxwMPhBnWJFv7AilH6SrENipyY5tPPpk5fc7xiffTXH89k2kVxVPrN9wQCuR/8UVY3u/XTzriiJqSVcgvL2W4da3/v8QSLP2Xg803DxUfspWBc2WNAw9MemRAKuM1glXE1km/777M76meAHIg63MoACDGpEkhLzUu/7tnz5AqAJQp6qwiL7xxO1ugGk0AeJINAJCD//43e6BqXsb6/POkRgSkHtUAkJE3I8e1zvPX3bkQJcTtZXwV4i5N3hS15ZbSqqsWe1RAZci141ibNoUeCVAyCFaRUa4JItlKQSJl7r035MF515xrp/pBPv10qXdv6f77Q0FEAIVxxRWh7nAuAe1uuyUxIqAkEKwio9VWiy+t6Y1W3tSKEvDEE6EkUn3dj1zr0bVU3VKVDTxAYappuJJDHOeresXjsMOSGFXF8ybie+4JaW8LLhgqh7k7L9KFnFVkLa251VYhIM2WBuDmREg5X3Ecf3zNx3V5p/nHH4fG3QDya8YM6YQTcjvWFTaefDK8AaNgvCJ40kmhJKOLmZx/vtS/f9j7NmBAfAockkWwiqy8ecov5rppVtHnF18sLb10UYaGhhgzRnr//ezT5J5Gv+66JEcFVIZhw6Sffsp+jF9/q68ujR8f6gWioByonn12WGDy26L/7wDW1+0+r/3rX8UeIWojWEVWDlTfeCO8cGtXllhrLenxx3lBlwyvccXxO7bL6iRt2jTXMMk9SRooNV99VX83str8/HfTh7ZtkxpVxfrmG+mCC7I/FFEpaaQDwSpiOY9nyJCQ2+P33J9/ll54IdS1RolYYIH4Y3wy9YOdBJ8NbrtNWnnlsOzpE7SbClx66cy5tEA5aN8+/mLMy1VJvf4q3J13xm8MdurwLbckNSLEIVhFzrx53DOtdHksQSutFILBuNmdffYp/Fh80vbmkT33lN59t+Z+15V00tjWW0t//FH4cQBJ8ZW9Z02zcZLk7rsnNaKK5kmXuApiDlZ9HNKBYBVIC+dbnHyyNHCgdM01YWk8XxykOkEr0+yOd9F17iztvbcKziWyrroqfFx7esNj881tKD2VD5QLrx5k22Dl159XGViuSsT888fPrPqtyBPiSAfarQLF9t130o47Si+9FE5aDiyd5e/yNV4WP+CA/P0sB8FHHhmW2qOpBf8s75JzEnKXLiq4tdcOHSeynS0WWkiaMCH3AupA2vlU64DVyZKetrPotb7GGtIjj+SWroMmcy6qW4THRT/el7rcckmNqvJMaUC8RrAKFLukjQvafvRROGllSrDaddf8/UzvSr711pk7WPXpU3MCLSS/3Tggz6WThM8oiy9e+DEBSbdbvfHG8Pz2OcqFPb37Py5FB3m1774hJ7W+tyK/FW67bVgEQuEQrBKsolR4k5FzN7NZbLGQz5lEMJmmYNW/cxIzvQAqcp7AAavnAvyW5LemaKJ7m22kO+6gP0qa4rUyOPsBJcwzLHFB6JdfhmXzcuCzgZc845b3nQZAUXQABdKiRQhIvdR/9NFSv36hFKOb+D30EIFq2tBuFSgm1zXNZZYxlzqppeKoo2Zu+1qXg3e3lCFfFUCBOSd18OBijwJxmFktA451PvtMGjtW+v33Yo8GDdKxY27L++XUrHqnnaQDDwwf1/7dPevq24YbSsccU7ThAQDShWC1xINUbxbv2lVacsmwodsxjSsfTZ5c7NEhJ65rGjez6pJSvXurbDggvfpq6aabZt5q681UF10UqhI0b17MEQIAUoQNViXKj5rjnPo6bHj1tEcP6ZVXKOCfep4Kd8H+ceMyVwPwgxy3CauUuZ6sC6L7ycqOaOTKzxlflc81V7gBKClssKoADz6YuRWc38M//lg65ZSkR5VCrtXpIvMvvpjOHAmXjnruOalnz/C5t6W6VZiDNs8uXn55eQeq5jep+eYjUEVu3PfZ9Updsd3V3d0ZarPNpBEjij0yAAXCzGqJ2mCDUEPegWkmfg//9tv4Ln9l6dNPQ+vOJ5+sqfzsmTvfd+KJIShME4/RU+EPPyxNmyYttZS0xx5Su3bFHhmQHt5o6JQYX4TWfvPzcpLTaa69Vtp//2KOEE3gh9CLTC4r5ap1bvyF8kWd1QoIVtu0kaZOjT/u7bdrJu0qKlDt1atmebk2z955J7prljCTB5SWrbYKF6CZrtK9Ye+TT0J7IpRUkHrFFaG51/jxNYtOTnU74wwae5Ur0gAqQK714dM2gZgIz57WF6iar83uuiuc8ACUVucnb77LtpwUbd5DyfBb8sEHhy7QUaBqztryRLnnHdyRGpWNYLVEbbJJfCC64IJho1VF8fJgtpmXaMnQl/EASseoUfHN3P26f+GFpEaEPBg2TLruuswPp9/Sjz8+6VEhbQhWS7iueqbN49EEg7txeK9ORXGx2VxOaO++m9SIAAAZeA9ptv4fPs85a+unn5IcFdKGYLVErb12yO+x2jOsUXqAU7sq8mrUiU65oNQNUFrcpjcuz9xRz3rrJTUi5MHrr2dfCLM//pA++iipESGNCFZLmJv8uFrL5puH2MuzqC7Z6VrrDzxQofmqq60WX1zWJ7TttktqRADyYbHFpC23zP7G5lWVQw7J7891JPXVVyFxsrz2I6dCrqt/FXk+wz8IVkvc+uuHake//RauPkePlvbeu4Lbqntm1RusMs3A+H7/cfJ9QgNQeNdcEzq61X2D8+d+bfvrbumXDy4hd9pp0kILSYssEtoDuuPajTcStObRppvGB6Kef1hxxaRGhDQiWEX5cR1Vl6ey2ic1f+zLeE87u4gfgNLSsaP0xhvSsceGRhLmINU7Tt1cI181Vh2oupj1mWdK339fc7/XovfbTzr8cALWPHEVgGxpAE5tO+KI3DO8UJ6os4ry5Ke1qwJ41783UzlPwkv/nlElUAVKnyMcl6jzazvfkcxxx0lDhmSPoryktfXW+f25FcoT4n5r9nxCtHHYQarrr7o52UMPhYZ+KC80BSBYBQA0hgt8esnfgXAmjqo88zp8eJIjK/vKZJdcIj32mPTnnyHjwjOqu+9Ovmq5aki8xlMAAIDaHfCyBarmGdeRI5MaUUVw8X+XqALqQ84qAACRXNsw064ZSAwzqwlwooVz/33V+MMP0sILh57HrrLE+x0ApEj37tL880s//pj5GK9LuxQLgEQws1pgP/8srbOO1KePdMst0iOP1PQ73nHHkB4FAEgJ7+RxsmTUYaU+3gXkNoIAEkGwWuAZVW9Af+218Hm0yzH6v3c4Uu4TAFJm0CBpww3D0lft5a+oFN7JJ4cZCACJIFgtIOffv/BC5uonLsvh2dYvv0x6ZGiy8iqiAaC2Fi2kxx+XLrsspAWYg1b3ufby2BlnFHuEQEUhWC2gu++OL7nh97/77ktqRGgS52xceqm01FLhgXV9RzcfcHNrAOWXDuDi/2PHhte+WwQ+/7y01VbFHhlQcQhWC5yvGjcB51WlX35JakRotKlTw4YKt3L95JMwLe4TmLthrbGGdPPNxR4hgELOtFLsEygagtUCWmyx+GOcv7rookmMBk1yzDHS6NHh6qP2FYgfQH/uFowff1zMEQIAUJYIVgvI5amydeuLLth33jmpEaFRPPV9003xDazd2hUAAOQVwWoBLbFEqICSrZbqWWdJbdsmOSo0qg/gjBnZj/EM61NPJTUiAAAqBsFqgV18sXTCCWEGtXblk9atw9cGDCjq8JCLuOnxiMs6+IH11cdOO0kvv1zokQEAUPaaVVWVVw2eKVOmqG3btpo8ebLmmWcepWmzleuqRh2sttlGmnvuYo8KOZk0SercOWyqypU3Y3i29aKLuCIBSln05u2OVossIm29tdSqVbFHBVRUvEawCuRihx2khx/OfZa1Npe7WW+9QowKQKH4tX7SSdKQIdKff4a8dN/Xpo10zjkhxwtAIvEaaQBALlwcvFOnhpev8fH/+U+hRgWgUI48Ujr//FBf1XM60YXqr7+Gr11ySbFHCFQMglUgFw5U33hDOvDA0AwgV04FGD68kCMDkG+upXzlldkLZf/73yFwBVBwBKtArjp0COWpvv9euv320CQg14AVQOm48cb4VZTp02k/CCSElhxAQ3iTlTdMXXNNTWmHbJzn1rNnEiMDkC/jx8e3H3Qw6wogAAqOmVWgIZx/6kDVctls5eDW+W0ASse882YvkB29/uebL6kRARWNYBXIlXcEDx6c+/E+2W23ndSvXyFHBSDfdtklt/QdVwkBUHAEq0Cu3npL+u673PNbzz5buuee3NIFAKTHOutIa62V+bXr9J599w11VwEUHDmrQK5++y234+afX5o4seFlrgCkg1dFXFd5q62kkSNrmnxE/99+e2no0GKPEqgYnE2BXHXrFk5i2TZeeCZmhRUIVIFS54vOV16RnnsuVP+IOljts4+02mrFHh1QUTijArnyiapHD2ns2OybLg49NPv3+eAD6e23pTnnDJ2tOnbM+1AB5IEvTjfaKNwAFA3BKpCrd97JHqjawguHJcL6fPRRaCrg2ZqIZ2B33TUsKdIeGACAWbDBCsjV5ZfHL+9/843088+z3j9unNS7t/TaazPf7/y3O+8MMzcuMg4AAGZCsArk6oUX4svZOA1g9OhZ7z/xRGnq1Pprs/q+N9+Ubr45f2MFAKBMEKwCuYorEp7pOG/MuP/++EDXvcgBAMBMCFaBXHkzVFwagDdNrbLKrK0b47pducKAUwUAAMBMCFaBXB1xRPbZUZet2m03qX37me9v3Tq37z/33E0bHwAAZYhgFcjViitKQ4aEj+t2tvHnSy0l/ec/s/67JZcMX8uWRuAZW7d4BAAAMyFYBRri6KOlYcOk9devuW/BBaWTT5ZefVWab75Z/42DVG+wytRMwF93sHvkkYUbNwAAJYo6q0BD9e0bbr//Ls2YIbVpE3qFZ7PHHtIXX0innFLTstH871q0CBuwundPZPgAEjJlivTdd+Ei1h2xADQKM6tAY7VsKbVtGx+oRjz7OmaMdPDB0uqrS2uvLZ1+uvT559JmmxV6tACS4gYg/fqFANVtmp3HvvHG0osvFntkQElKJFgdOnSoFl98cbVs2VK9evXS66+/nvX4e++9V0sttVT18csvv7yeeOKJJIYJFN5yy4XmAqNGSS+9JJ10Eu1WgXLimsmrrSbdd9/MGzJHjJA22MAnuGKOrqK5KIuvFx58UHIYkikzCxUYrN59990aMGCATj31VL311ltaccUV1bdvX33npZF6vPrqq+rXr5/2339/vf3229p2222rb++//36hhwoAQOM5+nH7ZKcI1a0c4kjJX99rr/q73KGgrrtOWnTRUIHQHbF79Qr7Xh99tNgjQy6aVVUV9trCM6mrrbaaLvdskqS///5bnTt31pFHHqkTTjhhluN32WUXTZs2TY899tg/962xxhrq2bOnrrrqqlmOnzFjRvUtMmXKlOrvP3nyZM1Dr3UAQFKefVbq0yf7Md5Q6aoi/fsnNaqKN3iwdPzxs94fFWi56y5p550TH1bFmzJlitq2bZtTvFbQmdU//vhDb775pvrUevHONtts1Z+PHDmy3n/j+2sfb56JzXT8ueeeW/3LRjcHqgAAJM5ry3XL2tUXITkNCImYNEn697/r/1o0VXfIIWEyHOlV0GD1hx9+0F9//aUOHTrMdL8//+abb+r9N76/IccPGjSoOiqPbhMmTMjjbwAAQI7iAtXapeqQiBtvzP51B6zOynAeK9Kr5EtXtWjRovoGAEBRuf5yXGtlf923yZNDNREU1Nix8ce4S/bHHycxmnT7++9wi+sqXnYzq+3bt9fss8+ub7/9dqb7/XnHDDugfX9DjgcAIBVcBWCVVeLP9nffHXb35BJJoUlatcrePNAcoPm4SvXMM9Lmm0vNm4fAvUcP6bLLQhnxighWmzdvrlVWWUXPOun8f7zByp+vueaa9f4b31/7eBs+fHjG4wEASAVHRS5NtcAC2Zf6vfb8/ffSJpt4c0eSI6w4W289a2GGujzRvdVWqkgXXRRKAD/9dM2iwKefSkcdFZ6e06erMkpXuWzVtddeq5tvvlkfffSRDj300Ord/vvuu2/11/faa6/qvNPIUUcdpWHDhumiiy7S2LFjddppp2n06NE64ogjCj1UAACapksX6Z13pEMPzX6cIwPvsXjggaRGVpE23TTMFGaa7PY1hXuyLL20Ks6oUdIxx4SPa2ev+FrKt5dflk49VZURrLoU1YUXXqhTTjmluvzUO++8Ux2MRpuoxo8fr6+//vqf43v37q077rhD11xzTXVN1vvuu08PPfSQlnMxdQAA0m7BBaVllolff3ak9PDDSY2qIvlP/OST0iKLhIcjekiixoMrrSTdfrsq0mWXZc9YcXrE1VdLv/2m8q+zmua6XQAAFGx91cU94zZcbbON9NBDSY2qYk2bFoLSW2+V3JPIDQIOOEDabruQq1mJOnb0nqD44157LTRRKGa8lsI9XwAAlDivK8cFqp7280YrFNzcc0sHHRRuqJk5zedxJZ0GAABAxenbV+rUKXsqgIPZAw9MclTAP3r3ji9c0bKltOyyKjqCVQAA8s2zpm5I72A1SpCs6+STpSWWSHpkJc9F/C++WNp777CU70pgFFVoOO9bz1YpwU/hffaR0pBRSc4qAACFLGJ59NHS++/X3OcNxg5UDzssfhMWZnLbbWEy2jVAfQ3gP58DroUWkp54QurZs9gjLB1VVaEawJAh4e9YOxp0oOoqCq4IMN98xY/XCFYBAA3jaay33w4Rg89odVpkow6fZl3Oavx4ad55pbXWSmeboJR76qlQZqq+qMXBlU/5viZw9gVy47+lN50NHix98EG4z0/Rgw92O/vCNllrSLxGGgAAIDfOsTznHGnhhaU11pDWWy9EBjvuKP33v4U9o3rH/EYbhYjEUz277CK98opKgqetXCPJO//9NyNQbRTX/Mw0Ee2n5pQp0tChSY+qtDVr5nr30pgxkquI+mXsCgHnnZeubsDMrAIA4vlUseee0h13zDq15Wmtdu2k11+XFl88vz/XW5GdmHjjjeHnRDvsHfB5/ffCC6WBA5v2M776Sho3LvTcdFBJMJk6X36Z21PL11ETJyYxIjQVM6sAgPxyQqALVdY3v+EA8qefQm5mvl15ZQhUo58TiXaGOOnu+ecb973HjpW23FLq3Flaf31p9dVDAU7v3qn9e/pjB+J33ik9+mgo2olE/fhjbsf5aYjyQ7AKAIh3xRXZ+907kHzkkbCWmM9ZVRfXz7YJybOg//lPw7/3hx+GVIZhw2YOTD1+B91Ri28Hwu6g6Krou+0Wms27mvopp8TXUUXeeANVLshXLU8EqwCAeN4gFBecObj86KP8/Uwn0H3xRf2zubVnWJ9+uuHf2zvxp07N/Ds5OHc/yo03DjOwtfnfnXVW2JZeXpl0qQ5W/VBku15ydQDK1pYnglUAQLwWLfJ7XC7+/DO347IVi6zPJ59IL7yQPfj2jO2JJ4YAvL4WPg5SnZ4walTDfjYa7eyzQ7BaX9laP1zO5qBDVXkiWAWKzSdalwEaOVL64Ydijwaon5e/s01rRTVvVl01fz9zscWkNm2yH+PIZfnlG/Z9a9c8zfa6/PXX7L0mHSFdc03DfjYabbXVQtZGVCltzjlrnpLeF/fii4WrCYriIlgFisUnQVdj9oaOlVcOve+81tWvX6jHCKRJVMA+U/6o7z/qqPzOrLrXoysBZAuS/To68siGfd98jdEBbT7THhBrgw3C2+PDD4eJ7zPOCJPb3v/mt1KUJ0pXAcXgl52Tq66/vv7ZmqgMkGeWIp9+Guq3ePbKwW2mFo5Aodx/v7TrruH5Gy2hR+WkdthBuuuu/Jd9+uWXcCHnpfu6y/Z+DWy+ufTggw37uZMnh01Sv/+e+Rh/72yzqlGA3qdP43JmgQo3hdJVQAm0YKwvUI1ma1x/pX//8Pmrr4aON927hx0GXgvr2rWmnE9TfPxxmJ7Ybz/p+ONDZWggEwekXkI//PAwjbXggmGq64EHpHvuKUx9Ul+cufj/IYeEOqiR9u2l004LP7uhP9fVzt2iJ9MFn4PQ5s1zm4F1QwQABcXMKlAM220nPfZY9o0hPpHefXcol+MZpfpmedxNyD3xGrNxxTsRbrpp5hO9x+OAxP335pqr4d8XKCTvwvcMq5+zSy0VAsrG8qzqttuGHp61mw34YydDep35ySelSy7J3N9z/vmlzz6TWrdu/DiACjWlAfEawSpQDJ6VmjAh/jjPXHnTVablSM8A+WTZpUvDfr5nla69tv6TsIPk7beX7r23Yd8TKDW+OHNqw1VXhbqrc88dZkqdn+t2Sb6oc9pDNHvr46OcXc/sPvtswzd3AahGsEqwirRbcskQZDaVZ3e8fO+aLrny7gSfiONe+l7uXXbZJg8RKGl+nYwYEXb9u96qUwh23jm0nuUcAyQSr9EAGSiGrbaSLr88exqA8+U8s5Ntk4eXLnMpw1ObUws8expXY9KtNZ1mAFQyz6RuuGG4ASgKNlgBxeBlxmwcTK63Xvzsp49zeZ+G+P773CoJ+DgAAIqMYBUohm7dpDvuCDOYtTc4RUGky/Fcemn89/Gs6xZbNOxnu3l2XNtMB8k02QYApADBagXxivI33zhPpNgjQbWddpLefTfUW3XNR7decYmqO++UHnpI6tEj7FbOVBDd97uVi/PnGsIbRuJmVh3M7rVXw74vAAAFQLBaAb77ThowIFRZcYMk7w9w+hV1rFNgmWWkK66Qvv461FZ1v0AHk1GAesMNUs+e4ePaAaY/9gPp3oMNTQNwYDxwYPYcPQfQSyzRmN8IAIC8IlgtcxMnhlbdXlF2m+uIY6K+faUrryzm6JBzQXTXQ+3VK1xtOMA988ywMzkKZBvKG6dOOCGkIDjwdV1J/9+3Qw+Vhg7N928CAECjULqqzDmd0TOomTadOzZxEyNXUsL/+CXhWU7PMHppPlMv9HLw7behOsCkSaGm6y67SAsvXOxRAQDK3BTarcK++CI0YMlWHclxmOth438Fwl1OypufXPDbeRPOG/UyfdyGpFLlnNd//Us677yQK0KginL00UfSUUdJa6whrbNOWJlw6g2AkkCd1TI2alR85SPHYE4JqHj+Q3jDk1ss1jZunHTEEaEo+F13Zd7sBCCdBg8OjTOiDlT26quhkYa7tLnmMYBUY2a1jOW6el3Oq9w58/SyA1VH97Uj/Ohzt2S87rpijhBAQ91zTwhUrfYSk0u+/fGHtMMOoc0qgFQjWC1xbl7kFS2/H7vVe+2yVL17xweinijcYANVNgejl1wSf1wuxwBIB7+uzzor85tgdCHK6xpIPTZYlahffpF22y3kpDrg9EYpTxy4itGQIdIhh4TjtttOevTRzCmX/rde6Xar+IrlP6Y3UuXCVwNt2hR6RACa6ssvc3tjcwk4vwcASBQbrMqcA083OIrqpPpzF/z3Zcf06aHykCsd2dVXS126zJpq6fQtTzi4jGdFB6oNlcacCS9nupju778XeyRAekydmttxv/1W6JEAaCKC1RL0+OPSyJHZN6i7hKZnWl2N6PXXpZNOCh+bZ2G33FJ66SWaFP0zs7LUUtkDUX9tueWk1q2VGp4S339/yVek3tXvGV83FHBXLKDSLbJIqB+cjV/XXbsmNSIAjUSwWoI8axq3Kd3lM597LnzsFe7TTgutVj3ZMGOG9OCDobMn/nfC6t8/e+kEf83HpMXbb0urrCLdckt4QM1XJ94Itvrq0jPPFHuEQPEvQl032MtI2Rx2WFIjQiNNnhz2t558snTRRSHDoy7vk/NbtJvdbL99OE96pRHlgZzVEuRY5I034o+78UZpn32SGFEZ8O7g3XcP5akcvEYvC09D+2tOEL711plbnhaLx+NasH7Hrm963WP0LKsL/bdqVYwRAukpNu0Wfo526r5WfMXv1RJ3iJt77mKNEFn4bfjii6UTTwxZTr7u8MPo+/fcM6S5tWgRNhhfcEFNdbLobdsN/5wu54cZ6UPOapnzim8uMVO07I8c+A96++2hpILbmUaWXVa6/vr0BKo2fLj0+eeZ80D8Lu2TswNvoJI5Yd85Uy6NUjdQdV1l108mUE0t92hxrxLPkDpA9d4Mv73549tuC3MI3lDsQLV2dTIfY07l32gj9s+VA2ZWS5DrWO+8c/Zj2rULDVqaN09qVGVm2rQww5rGmUnndLigebbWZJ5icEKyA20AYZ34rbfCa2PFFcOM6sSJoVPdjjuGaTikhve9dewo/fprfLaHr80z8dv4f/4TGpihdOM1Olg1gdvHO3B0ULjAAuFCPYnZzG23lZZfPnQQzBSvnH46gWqTlMNsSxorFwDF4hWTpZeWzj9f2nffkOsdrSsffXSo9+eoJm5TFhLxyCPxgaonyLMFqubpuDvuIFgtdSlZ1ywtXmI49dRwIe4yUeecIx15ZGirPnBg4dvI+73U+2e8v8b8fuubV6n9f0+6HX54YceAIlp77eyzquav+zgANRyoDhoUEiBrryv7TfuKK6SDDy72CPE/X30Vv5E413MtaQCljzSARvCORDdGyTSZ5UDxsstUcH7kvJL1wAPhCnTJJaW99w5LJyhjPrn26BE2j2TaYOW1Mb/bzzVXMUYIpI8jFs8wxNUjdrqAZ2BRVDffHL9B2MFsXMDqYzbZRHriibwODwnHawSrDeSEbc+gZpvYcsDq/S8U20fBvPeetN56oRZZ7SdjNM3ud+aK76ML1OLNk545zXbK82vHO3o8A4uiX1t44iWqzJdtD50Lo0SbqurjUo1On0O6UA2ggJz7ku1FEU1sufwlUDArrBBqrR50UM3sqfNDvD129GgCVaAurzTE1Vx1IOtNVyi6eecNaXWZUu89Y7rmmmEPqc+59RVr8X0bbihttVXBh4sCI1gtQB6NX1y836HgPHU/dGjIAfn557B91mtnLrcFYGbe9R+3Zuw3bx+HVDjzTOmII8LHPu/6Fl1vuBrZY4+F6/Inn5Q6dw73R8Gtj3VBlEcfjT9nI/2oBtBAfh+Lm1mNjgNy5gL+11wT1qtcNsszp96916dP/K5+vxN7GqKpvNnEW2vdUMCVtoFyssMOYdd/Nk6p8eoEUsEzo5deGnbyu8mNl/vdkdGNyRysRm+Nfpt06p3L5o4dGxabNtuMamTlhJzVBvKeliWWyJ72FKUUurwUEMt9cbfcUvrjj5qZn6gVi0+czikp5NSAzwCDB4ezgatv+2dtt510wgk1JSeAcuDdr1deWf8buJ/3nqZzyyPKvgEFR85qATmZ20sLmZoZ+f6oDiqQU16JA1XvUK69RBltmrrzzsylJ/LBxXpXXjnM6kaNtD2Ohx6S1lhDevzxwv1sVA4Hh+685jdH71BdbLFQ1/T995Mdh3t3umRKdEFYe115/fWl++8nUAVSiJnVRvDuRJfUcDdLv8/5L+j3N8cXjjt8fznUlEcCTjklFOrNlkvnMlTffCO1bJnfn+0nrtMNHLDW9/OjDl4OqD0GoLHPs8MOk666qmbFwPyxc6quuy4U6U+Sy1PddFPYXNC+fVjB6NWLQBVIEKWrEmq36qV+r9BGHaz22ENaddWC/kiUm+WWkz74ILdUgXzv8HeR3rjGAT55O2ks2uUANJSL7WfrUuLn2Ouv8+YJVJgptFtNhielLryw2KNASfNmqlxES/T5Dlbjqmo7kHj5ZYJVNI5nTi+4IDyPMs2L+Dl4ySXSrbcmPToAJYJgFSgmJzdPmBBfUscdq/It1yVPlkbRWOPGSf/9b/ZjnBbgRvDO23722VCGzTmta62VeXMAgIpCsAoUk8tTuRBgJp51WnfdUIIi3xwMxAXJng2LSxUAMolrbRpxjWC3K3LptNq7WT3jSkV3oOJx2QoU06abSjvvXP/spQNVFwy87LLC/Gy3f3EuS6ayWNEGqz33LMzPR/lzwJnLxkDPrtYOVM0zsttsE2oPA6hoBKtAMTkgvP126cQTpboJ5uusI40cWbiOVP7Zd98ddvrXbUPpz327555ZxwXkyg0mfLET1+a0PlGOq0tcuWEFgIpFsAoUm0/k7ivo8lTPPBPSAj79NLRjcbWAQlpqKemdd0I6QlRvzeNxt59Ro6TNN1dZcdBz773h93K+8MYbh5IeuS5Xo+H83HYroWwz+NkC1u++C/00AVQsSlcBCJy/OmVKCFqbN1fZ+eknqW9fafTomioI3sDjHevewObNPS5Yj/xzfb+BA8OFQlRn1e/PHTqEC7Ns/Fidf3749wDKBh2sADScgwI33i7HQNX69ZPefjt8HG0sc6Bqn30WOnqU17V7enhm9Y47pEmTworBSy+FAHb11eNbCfsxat06qZECSCGCVQDlb8yY0PM9U/UDz/Y5HeL555MeWWVx9xS3NXWFCW/e2267+IoUThOgIgBQ0QhWAZS/hx+On8Fzri47z5O19dahLFumDVhO03BrwE6dkh4ZgBQhWE05T/YMGhQ2xLqFvFtZo0JEm4Fc3mrppUN1gKuvzr3rFWr4b5ZLgXn+tsmac84w4x3lCkePURS89ukjXXll8cYHIBVoCpBSv/4q7bqr9MQT4X3bK2FO3Tr5ZOmYY6Rzz6W5S1lzzUkHqa+9VrMZyE8Ct0j1VYvz/rp2LfYoS0f37vHlj/w3LkSnMGTn5/GHH4Yyas5r/eGHMNt6wAHSJpvwRgeAagBp5EfEFXWcPpcpneuUU6TTT096ZEiMi6E//nj9TwBfvbjY+kcfxS9to2bG1B2Spk7Nftz110v77ZfUqACgYk2hGkBpe/nlUEUn276DwYOlX35JclRIjEv5uFd6ts1APsbBLHLjclxDhsQfd9RRpAIAQMoQrKbQbbfFN3yZMYO9IGXLTQHilj79BPGmIeTOu8/jeOb1rruSGA0AIEcEqynkhi1R3exMHMv4OJSh336LD1adwOzjkDvXWPWGnmz89bfeSmpEyJVXGb76KnR5K6/MNQA5IFhNaf3suJlVv3f7OJQht0CNu1rxZis2AzWMA9FcAp24gBbJmT5dOuusUC1gkUXCm56f91ddVdPQAUDZI1hNob32io9V5por1NNGmdaebNcuvmf6/vsnOarS553lcS8sVwzwcUhHoOqdpqeeKn37bc3948ZJhx4aNsIxywpUBILVFOrVK3R+zLYS7GoAbdokOSo0iafCvSHKPc4vvlj6+OPMx7rdqeupWqaA1aUgOncuzFjLlTsnLbNM5mULV1ZwGaW+fVW2nOzuuqb33CO9/nq6g73zzpNGjpx1BjUa8803h98DQNkjWE0hxycuObjjjjXnUK9MOnj1edZxyvHHF3uUyNnw4dJii4UrkJNOkgYODEv9m20m/fhj/f/GD74rAnTrNvP9Lr/kIuknnpjI0MvuheVNaW75WfdK0J97Nvuxx8qzHJgDvAsvDMvoDsZ32SVcFTt4d/CaNp7hHjo0+1K/H6dLL01yVACKhDqrKecKRZ48+OmnEO/stpvUvn2xR4UG1SHbYINw0q174vWVhztTjRoV8jrq45fnG29IEyZI888feqrHJTQjOxedd86ja6p6edkvKC8pH3aYtOCCKkvHHhuC1foCeN9cgWLzzZUaXuqve6FWH78W/vgje8oMSi77w6Ubfc5bdFFp3XXpC1GuGhKvEawChdS7dwhGs80QXXcd+acoHDeP8AxqJg70OnWSvvwyPbPKn30mLblk/HFecnJqA8FqyXMk4iwpZ3+4gV/EAaszp9ijUX5oCgCkZVq8vpy72jxlcM01SY4KlcYXQ9lm4x0luCyU01XSYvHFQwCdjQNrT7sRqJaF446TBg2aOVA1Lyptvz3pyZWOYBUoFL/LxnEgO358EqNBpRo7NrfCzdk2/SXNgei//pU9EPWmRXccKyaP4fPPw4Wp0xHQKJ98Un+WikVrv87S4U9cuQhWgULxhp1czDdfoUeCSta6dW5NJtySNk0GDAibEqO82kiUquA8XH+9GBz8X3RR2EiwxBJS9+5h86M3PtKso8GcPh6XgeK9qN7/iMpEsAoUyoorhhNZttkhBxEurAsUyjbbxBfQd6SQpg1WUT7qAw+EzXC1c27XXDPc7wTHYqQAeDbV1RQcLDt9IvLzz2FM3lA5bVry4yph3k8X9xR1JosnsFGZCFaBQvGJ9IwzMteydIDg2dcDDkh6ZKgkO+wQavJmmrryBdPuu8fniBaDI5SDDpLefz+sAXtG86WXwm6bYuWq3nprCJbre107kB09OgStyOvkv/+0Pg6ViWAVKCTXGhsyJLwTR7coaOjQQRoxglpkKKwWLcLmKT/fai+pR8/DDTcMtXvTzjOtaahW4Nqu2SIrTxFecUWoFYuceAOVg9Fs/LT1IkFDvPmmtMceoYGOe60sv3yYqHcBCZQWSlcBSfBy4Q03SGPGhOBhiy3CO7TfQYEk/PqrdNtt0h13hCVrd+vyrKWbU6QhCCwFDkQdNMetWZs3XnXpksSoSp4nzJddNvzJ6tsL6Kdnv35hUjtXfqrvvXe4roi+Z3SdttZa0lNPSa1a5ekXQKNQZ5VgFQCQbz5dOliNmwY0V/mgJXLOXObXk/wOWB1g+nrAWSAONDfeWHroodyDS1cXcJpzpofJwe+hh0qXXZbXXwENRJ1V/MMv1v/+V/rii9zeXwEAGXhqzhuoss1E+xjPqC68cJIjK3kurPDBB9LNN4fgdNVVpW23lYYNC7eGzII6qyWu6pnLD9et6Yr0IlgtU96LcO654cLe75te8VtkEemcc6hVBwCN1r9//JX/0UfTI7QRWrYMxVEcnLrL9L33Sn37NvxP+eST8aWFf/9dev31Jg0XCeLVVIac17/11tJJJ0lff11z/zffSCefHNIlCVgBoBH8Bup6qlZ7hjWKqHbdVTr88OKMDdXiAtWGHofiI1gtQ14Cefrp+vcA+L5nn5Uuv7wYIwOAMnDWWdITT0gbbVRTpWC11aTbbw87e5hVLao11sjeYdj8ELkUNkoDG6zKjB/Nbt1CknqmR9a5PM4P8jG01QaAJvKbLW+mqTFypNS7d+avO5DdaqtQLhfFwwarCq9O89lnmQNV89e86eqXX5IcGQrinXekQYOkQw4JScoTJxZ7REDlIVBNFTc5O+64+h8aB6ouOexyuSgdBKtlpiHlEimtWMKmTAntMVdaSbrwwlDD1QnJnjI/4YTc6kACQJk677zwtti9+8wbuPbdN2ze8oZjlA7SAMrQyitL776bOV5xrs5yy4VjUIL8ku3TR3rhhcy7kk87TTr11KRHBgCpEq0kTp8uLbooLVvThDSACjdgQPaJNX/Nx6BEvfii9Nxz2cvneFqBIoIAKlxU9tZNAghUSxfBahnaffeQwlh3qT/6+MADQy07lCjvOI7b6uoigm75kjTnzJ55ZuhzeMQRIagur8UbAEDCYs54KNUrySuuCFVVLrkk7Ix0vNCrV6hnveOO7Acoad99F18g0FcmPi4pfoKdfnoIVKMnl/8/dGjIS3nsMWmhhVSWXMz422+lBRagaxEAFADBah4K8A8fLn31ldS+vbTpptJccxV7VCFOcFDqWzSxRYBaJjp2rGmanYlTBJIMDi++OASr9XnvvdA/8a23pObNVTZeey1sanvmmZr71lkn/B3ckhMAkBdssGqCm24K5TG+/77mPv9Id4465hiCQxTIq69Ka62V/Rg30vZsXxJJWk45cGAcVwvt7rulnXdWWXDXDXcy8ttn7dxhz2j7vnvukXbYoXA//7ffwhWyr4w9m8ubDYASwwarBFx9dSiBUTtQjSoKOYD1hEuxefejUwhdexVlVkTQgVK22mOnnJLcbgLPLMYFqh7rrbeqLLhX8W67hSC17iY3f+5g1Tm7U6fm/2e7Z/Jhh4VlHNfk6dxZ6tlTuuuu/P8sAEgJgtVG8Dlo4MDsx5xzjjRhgori00+lffaR2rYN57J555W23loaNao440GeeRbNM3fbb18TCLrlo2uSOT3gjDNqKmIn4Ycf4o9xEJdkDm0hPfig9OOPmTeO+X7PfN5xR35/rq88V11VuvbacCUaef99qV+/zGkYAFDiCFYb4d57w7koG8cNN96oxL39trTKKmHDuPNpo1JVbmO99trSo48mPyYUgJf5HbCOHRvqqXrn/ZAh0qRJYVo/yWXhXDYVOaB2kcNy4BeZLw7ifl/n6OaDH1PnFnkm1Uv/dXOVozp1rq375pv5+ZkAkCJssGoEtzP1BFYUDNbHsYKPS5IndJwS6EC6vtVJj2nXXcPm5Qrtl1B+evQofs6JNxM5Z9VPrEz8BNxvP5UFv/hzSfWPKy9mP/0Ukt+jBg9O8dh//7CJzhx8ugGE84viupL557n6gtv2AEAZYWa1ERzo5dLNsk0bJcolLceNy1wr3udXrx7edluy40KZc5A0eHD2WcZ115X69lVZcPAYVzrMX3cFhGwefzzk6Rx7bFjy8OfONfYMtF+kfrFutllIOs/lDcc/03XqkuK2QA88ID3ySEiLAIACIVhthO22y948KDpvuGxU0pV04iZznJ6Q5PkMFWKPPaTrrqvZ1OVl8mgDmBOmXWfVT75ysN56oV9xphdblPKw5ZaZv4d7HfuNxAGpA9FoptYfe8nGXTucg+odnHFvNnV/dhJB6uabS127hooH22wTZtY9I8xuTgAFQBpAI3TrFt6j3SCovvOIzxeug+5zWpK8zJ/L6iRVbiqAgwbvEPfmmxYtQnDhJ2QhH3wHK84zue++MMXvpYVttw25luXEf0O/+F1T1eXBas96+sXvHY2eJc0WOF54YXixZnrBOrC/+ebwPXINVn3sJpuooLzJa401wqa62mN3gO3x+vnmlIaWLQs7DgAVhTqrTagI4AmjESNqzifR/1dYIZRh7NBBiXr55XD+jOMJMMcVKFPehX7QQSF5Ocqv9FS/n5hesl1ssWKPsDw4YLvqqrA735UO5p8/1LNzaalsDRn8eDiYcwmsfPIbkDfcLbmkCsa/n1MUMqVBOJC/9NKw4Q8A8hSvEaw2gSdUnn027PofP15acMGwGuogNpe9FfnmR9LxyEcf1T8Z48ka/0m8odibyVGGvNzuJ2B9L2s/Kb1z3x2l2GFXPDNm5D7z6BdtXL5q1IjAM5t+AyoUb/JyS9lsQbaD1aWXlj74oHDjAFBx8RppAE3g84j3UMTto0iKzxNegXWJqp9/njlgdZzim/dDEKiWKQcsgwZl/rpnw3xV5d3n//pXkiNDbW4561nYuE1JTt/IVnIketHvtJM0YIC02moqKBeOjpsN9nPQhZ4BII/KZMcDalcyeucdqX//0BQgOuftuWeogkPL8jLm2SznDMYtllDaqLgcYB58cPacVl9Z+kXrJfXo39Tmf+ug1/mhd95Z+EDVcr3KdQtYAMgjgtUy5JVe79/w7Oq0aSF10fHJMssUe2QoKG/2ieNA1i07UVy+muzUqf58IQeiXhI78UTp8MPDckndF6+vOl95Jbck9XxZfPGwxJ9tk55/n622ksaMkT75JLeSWwAQg2C1jPmc4smQcqkYhBi57OjzkyIqOI/ice6ng8211qp5XKIgsGfP8DUHh+bSIw7+vHnKdee8HD98eGi9miSP79//zj5z79wjB9dOnvcyj8tbXX55bmVKACADclaBcrHssqH+p9MBsgUH5dJJqtS5IcDzz4fHy8v5fsxcFsr9kusLFB38NZVzTr3D0ikEntltaCkzb+ByTuoZZ4RZ1KgqQLQRzN/PG8giX34pHXlk2NR39dXUzQPQKFQDAMqJ63t6Gba+l7WXlx0guSA9r43KMnmydPbZ0jXXhI/NS/runrXPPg0PIt9+O7R2HTWqJmjNVIYk8uST0qabNu33AFA2KF1FsIpKRp1V1ObkdacbOIe0djAZdRHxzOcllzR+1vOnn0JqSbbKBX4eut2uS6sBgChdBVS23XYLtVa9SzzqYLXFFtK667IMW4mcZ1o3ULVonuKyy0Jr2MZ2v3IaQ1yJLV8svfFG474/gIpX0K03P/30k3bffffqiHneeefV/vvvr6lu/ZTF+uuvr2bNms10O+SQQwo5TKD8tG4tHXhgmDEbPLjwrVaRTi7k77q62ZbnPevpgLWx5pwzt+OK0SkFQFko6LuHA9Wvv/5aw4cP159//ql9991XBx10kO7wMmUWBx54oM5wAv//tKKKPQA03IcfSr//Hj/r+eqrjf8Zrl7gos5RLmymQJV81ZK6xvFk+XzzUU0G6VCwp+FHH32kYcOG6brrrlOvXr209tpr67LLLtNdd92lSZMmZf23Dk47duz4z43cU6CMTJwonXqqtPnm0jbbhFm9bIEOGi9b44HGHFcft4494ojsUY1ndumalmrOCnHmkItR+NqjfXtp0UWl886Tpk8v9uhQ6QoWrI4cObJ66X/VWrUA+/Tpo9lmm02jvIM0i9tvv13t27fXcsstp0GDBuk3bxTJYMaMGdVJurVvAFLqqqvCBi/vTPfu8EcflY46KlQpGDGi2KMrPy5lFnex71nPPn2a9nNOOSVsoLLaQau/t9NPXLZqxRWb9jNQ0EB14MCQ7u4OiBFXOXNvCj89CFhRlsHqN998owUXXHCm++aYYw61a9eu+muZ7Lbbbrrttts0YsSI6kD11ltv1R6u7ZfBueeeW72bLLp19kkPQPo4MD300FCPM8qh9FnSN7da8yawceOKPcry4tanhx2WfdbTaQBNnfV03VZXmnB+7Morh9nWNm2knXeWXnst5E8jtYYNk/7zn/Bx3aZj/tzzS7Uy84D0B6snnHDCLBug6t7GutNKIzmntW/fvlp++eWrc15vueUWPfjgg/rss8/qPd4BrcseRLcJ7u4CIH3OPDNz0OQzopPkLr20/q/7bLnvvqFrk6saDBkSSiYhnlMuog12tTfZRRue3JvZzQiayt9v773Drn9Pw3mV6/bbpdVXb/r3RkH5ZZctE8TXlldeGZ/+DBRKg+usfv/99/rxxx+zHtO1a9fq2dGBAwfqZ9f4+5//+7//U8uWLXXvvfdqu+22y+nnTZs2Ta1bt67Of3UQG4c6q+XNFXhc19xLVa7ItNlm0l57UeM+9XwR6QS4OO3aSbXfXxzEHn54SB+o2zHJM3dOJVhzzcKNu1y4c9X114f8YBfvj+qeDhggbbhhsUeHIvNLKaZQTzX3E3G5ZiD1dVYXWGCB6lucNddcU7/88ovefPNNrfK/9oHPPfec/v777+oNV7l6538JNAsttJBKmc+5Pl/7CtWZCrlWe0ENpzmedFKYAfDf0ZNEjlV83xNPSL17F2dcXsH2xgRP/jmG8iSW27k7mMb//PJLbsf9+uvMn3vWz4GqRYFq9ILysd5h7iuYDh3yM05PHXmG16W/yqnUl5fpnYLhm188fqIm/fu9/HJYa/aL1cHzMsuEjVmeMff4kHrl9JJAiakqoE033bRqpZVWqho1alTVyy+/XNWtW7eqfv36/fP1iRMnVvXo0aP66zZu3LiqM844o2r06NFVX3zxRdXDDz9c1bVr16p111035585efJkzxRX/z8N/vqrqurSS6uqFl88Ss6rqmrXrqrqxBOrqqZOLfboSsdNN9X8/ereZputqqp166qq8eOTH9fjj1dVtWkTxjHHHOHmjxdYoKrq1VeTH09q/fhjVdXss2d+EKPbEkvU/JsZM6qq5p8/+/F+8M88s+nje+ihqqq11675vosuWlU1eHBV1W+/Nf17o6pq6NCaF0n0N27WLNw22KCqavr0Yo+wom22WfzLc955q6p+/73YI0U5aUi8VtBg9ccff6wOTlu3bl01zzzzVO27775Vv/766z9fd0DqgY4YMaL68/Hjx1cHpu3atatq0aJF1ZJLLll17LHHNijwTFOw6kB1t91q3pNrv/D9xrDqqgSsufj776qqrl2zv5H67/nvfyc7Ll9j+dxb97GNxjP33FVVn36a7JhSbccds58RHXg6QIy88EJ8cOvb8ss3bVynnlrzoNUdz5prVlVNm9bkX72ivflm/S+S2n/n444r9igr2rBh8deESb+/ovxNbkC81uCc1bRLU87qXXdJ/fpl/rqXs4891hUNkhxV6RkzJrc8qcUXl774QolxR1OvaGZqDuS0QG+CvuKK5MaUat54udpqYfNN3T+aXwxLLBE250SvW/9xXSGgkA/8889LG2yQ+eteLnde5wUXNO77Iyzz33bbzGkcdfkx//prF9lOcmSo5fjjQ7M7P+VrVwTw52utJT39dCjyABQjXqM3RQF5L0NcnWyn482YkeSoSk+u9eKTrCvvn/X449m7WPrcfMstNS3YK5L/QP5DuZaqW7/6/z16hK/V3p3uQo4vvjjzTrkll4z//g5yl1qqaS/SbG1Afdb2jj6KTDaeo5xsgaq5coB376BoXPz/nnvC9WTEJZEdwA4fTqCK4qJZcwF5kqhuzbr69p38978152/UP3HmmCZb0OeLAk/MJcWPW9xjG22+8n6ditw/8vHHYWbUZeeiHYUOWnzWcxmreecNgaJ3o3fvPuu/931rr+0OI5mvCnz/IYc0foyeWc0lkPrgg1A2Cw2X7YqutrjHAQXl99iddgo39+Hxw+EqAWyqQhows1rinQ4rwSKLSJtskv3v5MCxKTFLQ80/f/YJuYjjsYoMVL//PpRF8JWYOWL3zVcc3nHvjke+QvODVl+gGrn44vAHrO/B9xWKa5dtuaUKrqKnx5vINVzjXix+jN1tC6ngbAwvchCoIi0IVgvIqXBxgejCC0tduiQ1otJ1/vmZYxbf50mv3XdPbjyubLTjjtnPwR7X/vurMrm9pgPW+mbVHPj5LHjyyfHfx2XvXnhh1qRlz866/uqDDzbtas8zt3GBlB9sl1lC47g8VbZZUz9+7vM533xJjgpACSFYLaD+/bOvgPl87RQ+Zlbjua24Y5a66RL+G26/vfTMM8nnVDnWcgBdX16yH1PPqh59tCrTjTdmz5OIejh+/nn893IS3VtvhZs7It1/f9iM47Y7TS1m6zajcYHUAQdIc8/dtJ9TyTbaSDryyPBx3ak6/319te7ESADIgGoACXQ6dE/lqJC9Rbst3cTLCe25LCcj8LPVrcbfey8Eit6X4yYLxeKxOMdr4sSQlunxOfZxUO1Jv6WXVmXya69ugf9Mf8AGNAkpiEGDwu6S+rZBu8/9iBFhdhWN5xeGO2i5qoKbOJj/pvvtF1JCnFcDoKJMaUC8RrCaAHdZcuMWn/N8LvQsoSd09tyTWdVy4IuQYcNC3OXH06ma669f4flezkP99NP445zT6i3HxearRnfL8q5I69gxpBl4apxZ1fzx6Wb8+FACxVeZc81V7BEBKBKC1ZQFq5HoL13RQQwqJ8n43//OnArgWUv3x33pJaWKd/67FWi7dtnrzgEAmoQ6qylVu6wkUNbcDaFTp/pzXKIXwllnKXX8htm+PYEqAKQI78gA8s8zky7yHyXtOmiNaq1655kTep0vAQBADLb2ACgM7/J2VyIv9T/1VFhe79lT2mEH2uEAAHJGsAqgcLzcv+664QYAQCOQBgAAAIDUIlgFAAAlXT7wkUdC52XXuF599VAu8uefiz0y5AtpAAAAoCRNny5tu6309NMzN98ZPVo65xzp2Wdn7daM0sPMKgAAKEluWe5221a7vbnrmntmdZNNpN9+K9rwkCcEqwAAlCF3tj3mGGnjjaWttpKuvDK3Lsil4rvvpBtvzNx7xMHrt99Kd92V9MiQbwSrAACUGffccP7mxReHmcfHHw8dhN3d2K2hy4Er4v3f/8UXJHFZZ5Q2glXMwlfew4dLTzwhTZxY7NEAABrCs40nnzzz0riXxX1zR2EvjX/1lUpeLsv7/p2nTUtiNCgkglXMlKjev7/UoUN4M9tiC2nRRaVttpHGjy/26AAAcbwkfuaZmb/u4NVB3tVXq+QttVT8MW6et8wySYwGhUSwimp//hmC08suC0Fr7atSz7C6FAizrACQbu+9J33xRfZjHLDecYdKnnuNLLGENFuWSMZpAgcdlOSoUAgEq6h2223SiBH1J6r7xf7jj9KJJxZjZACAXE2enN/j0sz5qNdeG4LVTAGrVwspXVX6CFZRbejQ+KvTO++UfvklyVEBABpi8cXjj/F7fdeuKgsbbBA2kC2//Mz3t2snDR4sDRlSrJEhn2gKgGoffZS5/EftVAEvL620UlKjAgA0hHf7b7SR9PzzM9cdrc3v9YccorKx3nrSO+9I774rff65NM880jrrSM2bF3tkyBeCVVRr2TK3nZVzzZXEaAAAjXXBBVLv3uHjugGruzytvLK0224qOyuuGG4oP6QBoJrb1XnXZLbcoC5dpO7dkxwVAKChvPrlmdVu3Wrev6P/77BDKE3YokVRhwg0CDOr+Kdl3c03Z/66qwIcd1z2vFYAQDr06iV9+KH06qvSmDFhSdydrDp3LvbIgIYjWEU175a8/XZpjz1CYBotHXm21ZurHMwefHCxRwkAyJVnUtdaK9yAUkawin/ssou0yiqhf7Rrq/7xR6ivethhIVkdAAAgac2qqjyPVj6mTJmitm3bavLkyZrHWwIBACiiGTOkhx+WPvtMat06dAV0d0Cgkk1pQLzGzCoAAAXi9Kojj5R+/jmkVblslNOqdt89tDxt1arYIwTSj2AVAIACuOeesA8g4vz/iNudfv99SLli4yqQHS8RAADyzJtUBwzI/HXPsD71lPTcc0mOCihNBKsAAOTZiBHSV19lP8ZpAddfn9SIgNJFsAoAQJ5NmBB/jNMC3MIaQHYEqwAA5Nl888Uf41zV9u2TGA1Q2ghWAQDIs002keKqJzpv1VUBAGRHsAoAQJ65JNUJJ2TPV+3WTdp++yRHBZQmglUAAArAweoxx4SPZ589tD91kGrdu0vPPiu1aFHUIQIlgQ5WAAAU0KefSjfcII0bJ7VpI+2wg7TppiGABSrVFDpYAQCQDl7uP/fcYo8CKF2kAQAAACC1mFlFwcyYId13n/TQQ9Kvv0o9ekgHHCAtv3yxRwYAAEoFwSoK4pNPpI03lsaPD7UEXaLFmwkuvVQ64gjpkkvohw0AAOIRLiDvpk2TNtqoptWgA9WoW4tdfrl03nnFGx8AACgdBKvIu9tvD4HqX39lPmbwYGn69CRHBQAAShHBKvLurrvij5k8WXruuSRGAwAAShnBKvLup5+kXKr3OmAFAADIhmAVBakpGHVpyWbxxZMYDQAAKGUEq8g7l6eKNlPVxy0HHdCuuWbDv7dTB7bbTurYUVp4YWmffaTRo5s0XAAAkGIEq8g7l6zafPP6S1M5UDWXsIo+zoXTCgYODFUGHntM+vZbadKksJlrtdXC9wMAAOWHYBV55yD1/vul/farSQeIAtdOnaRHHgl9sRvi5pulIUPCx7VnbaOPjzpKev75vAwfAACkSLOqqly2wpSOKVOmqG3btpo8ebLmmWeeYg+n4nkG9IknpKlTpe7dpT59pNlnb9j38DN02WWlsWMzb9xyUOzZ3IcfzsuwAQBASuI1glWknmu2LrJI/HFzzhlavDYkvQAAAKQ7XiMNAKnnADQXTgkor0svAABAsIrU867/uElyz6b26FH/pi4AAFC6OLUj9Vq0COWw4nJdjzgiqREBAICkEKyiJJx0krTEEvUHrJ5NXWedENACAIDyQrCKkjDffNKrr0p77y01b15zf+vW0oAB0rBhYQYWAACUF6oBoOT8/LP0/vthlrVnT6lVq2KPCAAaz5tDH31Ueuop6Y8/wvvannuGi3SgXFG6imAVAFACPvhA2mIL6csva5qo/PVXWEG65hppr72KPUKgMChdBQBAyn33nbT++tLEiTUzrFEJPpfs22ef0FQFqHQEqwAAFMFVV0k//RRmUjOV5Dv55KRHBaQPwSpQJD5BOU9tl12k9daTdt9devpp6e+/iz0yAEm46absr3d/7a23pE8/TXJUQPr8L0MGQJJ+/FHabDPpjTfCRjEHrv7/HXeEwNVBbJs2xR4lgEK/D+Tihx+kbt0KPRogvZhZBRLmfLTttgszJhYtAUb/f/llaY89ijc+AMlYaKHcjuvUqdAjAdKNYBVI2KhR0ksvZc5T8/2PPCJ99FHSI0McPzb33SdtuKHUsaPUpYs0cKD02WfFHhlK0YEHZm8R7a+tu6602GJJjgpIH4JVIGH33ltToiYTpwTcf39SI0IuXP9y222lnXaSXnxR+vZb6b//lS65RFp2WXZto+Hcdc+BaH3vBw5UfTv77GKMDEgXglUgYVOmxB/jk1QuxyE53pUdBaS1Z8X9sQPZ7beXJkwo2vBQgtq2lV54QVpppfC5g9Y55wwft2sXctfXXruoQwRSgQ1WQMK6do3f8e9aiz4O6TB1qjR0aObHzXnIfsxcioiZMDRE584hNcibLd02Oupgtc02NYErUOnoYAUkbNIkadFFM+esWsuW0tdfS/POm+TIkMnw4dImm8Qf53QAtwIGAGRHBysgxbyz96STsh9z7rkEqmnibkK5+P33Qo8EACoPwSpQBKeeKl14oVT3YnK++aQrr5T69y/WyJBpxjSO8w1XXjmJ0QBAZSENACii6dOlJ58MPcI949q3r9SiRbFHhfo4DeC557Knb/jrG2yQ5KgAoPzjNTZYAUU011xhFznS7/LLpTXWCFUa6gtY999fWn/9YowMAMobaQAAkIPu3cOu7c03l5o1q7l/wQWlwYOla66Z+X4AQH4wswoAOXJ/dncXc0WHTz8NM+OukUmJoXRyktvIkdL48aFuqWe+mzef9Tg3dxg3Tpp7bmnVVXk8gbQhWAWABnJ+Mf3a080F9Y8+euZWuA5Y3dzhqKPCLPiYMdKAAdIzz8w8U3788eHfMlMOpAPBKgCgrDzwgLTjjrPe/9NPIQj9/vvQNtfdoeqWG/Nmx4EDQ5DrRhAAio+cVQBA2fjzT+mQQ8LHmWrduI7xPvuEQDVTdYcrrpBeey37z3IqiMvMucnHAguEShD33pu9YgSAhmNmFQBQNh57LMycZuPl/Xffja+b65rHrgBRH+cue/Y2arVrL70kPf+8tOWW0v33158fC6DhmFkFAJSNTz6RZp89+zF//x3/fRyAZgpovSHLgaqPiQJVi2ZUn3hCOuWUhowaQDYEqwCAsuEd/XGtbmbL8czXqlX993vG1QFvpp/jrznfddq03H4OgOwIVgEAZWOrreKDVQeTXuaPC2i33jpzCkBcXurUqaEuL4CmI1gFAJSNxRaTdt45cyqAg9QllpAOPjjzDKvv96yqu5LVp24FgUxmzMhx0ACyIlgFAJSV664LZaksClqjmqkLLyw9/bR04YXSxhvPfEz0ccuWYaOWd/jXZ+WV42dm/fOWXbbpvwsAglUAQJlp3Vp69lnp4YdDe9zll5fWW0+69lrpgw+krl1DQPr446HUlDtbOYh1S91Bg8ImLR+fyWGHzbyxqi4HvJttFkpaAWi6ZlVVcdk9pWXKlClq27atJk+erHnmmafYwwEAlBmfNQ88ULr++lm/5hlXn3pefz2kGwBoerzGzCoAAA3gJf5rrpEuuEDq0GHWTVlvvEGgCuQTM6sAADSS0wHeeUeaPl3q1k3q2LHYIwLKL16jgxUAAI3kZf9VVy32KIDyRhoAAAAAUotgFQAAAKlFsAoAAIDUIlgFAABAahGsAgAAILUIVgEAAJBalK4CAFSE8eOlO++UvvtOWmghabfdpE6dij0qAHEIVgEAZe3PP6Ujjwxdp9xlyre//pKOP17617+kCy+UZp+92KMEkAnBKgCgrB16qHTDDZL7NTpI9S1yySWhfeqQIcUcIYBsyFkFAJStceOk668PgWp9fL8D1kmTkh4ZgFwRrAIAytZtt+W2xO9cVgDpRLAKAChbX38dclSzcTDr4wCkE8EqAKBsLbig9Pff2Y/x1xdYIKkRAWgoglUAQNnaffeZN1RlClb79UtqRAAaimAVAEqcNwm99pp0xx3So49K06YVe0TpsdRSIRDNlArgSgAHHSQtumjSIwOQK4JVAI02caJ08snSKqtIyy8v7befNHp0sUdVWZ59Vlp6aWnNNcMs4tZbSx07SqeeGj+jWClctmrXXWvyU+ecs2bT1f77S5ddVtThAYjRrKoqU0GPpjn77LP1+OOP65133lHz5s31yy+/xP4bD+XUU0/VtddeW338WmutpSuvvFLdunXL+edOmTJFbdu21eTJkzXPPPM08bcAkMkjj0g77TRz3co55pD+7/+kE06QzjknzFpVgvffl26+WfrqK6l9+9AZqVevwv/+zzwjbbppWMau+07un+1A7NprCzuGUjJ2rHT77TUdrPbcU1piiWKPCqhMUxoQrxUsWHXQOe+882rixIm6/vrrcwpWzz//fJ177rm6+eab1aVLF5188skaM2aMPvzwQ7Vs2TKnn0uwChTeRx9JK64YAtNM7yAOkg44QGXtjz/C73jrrSFQ99/CQaL/LhtvLN1/v9SmTWF+tn+Wr+O/+CL7BqLXX5dWW60wYwCAxmpIvFawNIDTTz9dRx99tJb32mAOHDNffPHFOumkk7TNNttohRVW0C233KJJkybpoYceKtQwATSCl00dLGUKVB2wnXde5q+XC7fq9EydOUD1DLP/b889F2aeC+XFF6XPPsseqDqAZmYVQKlLTc7qF198oW+++UZ9+vT55z5H3L169dLIkSMz/rsZM2ZUR+e1bwAK6777aoKy+jhIdSDlZddyztd1IJgpWHTg+tRT0htvFObnf/pp/DF+jDwLDgClLDXBqgNV69Chw0z3+/Poa/Vx2oCD2ujWuXPngo8VqHTTp+d23G+/qWzde2/8MZ7Z9A79Qph77vhjPMNNNhSAigpWTzjhBDVr1izrbWzCUymDBg2qzneIbhMmTEj05wOVqHv3+K5ADtQWX1xl68cf49t4eobZxxVC375S8+bxP3/77Qvz8wEgKXM05OCBAwdqn332yXpM165dGzWQjq61Iunbb7/VQt6m+T/+vGfPnhn/XYsWLapvAJJz2GHZN085UN1hB2n++VW2FlkkeypENLPp4wqhXTvp4IOloUPrT0VwIO3uTVHJJgCoiGB1gQUWqL4Vgnf/O2B99tln/wlOnX86atQoHXrooQX5mQAaxyV/brpJevXVWQMlB0leej733GKNLhk77ywddVSoCJCJg9m99y7cGC68UPJikvegRmXDonJZLqE1fHhu6QIAUJE5q+PHj6+user///XXX9Uf+zZ16tR/jllqqaX04IMPVn/sFIL+/fvrrLPO0iOPPFJdsmqvvfZSp06dtO222xZqmAAawcvPw4aF2dW6S9Hrry+NGuULUJU1z2y6IUImDhr99+nRo3Bj8N/+gQdCvVUv9/s6f911w2yrN2Atu2zhfjYApHJmtSFOOeWU6nqpkZVWWqn6/yNGjND6PptJ+vjjj6vzTCPHHXecpk2bpoMOOqi6Luvaa6+tYcOG5VxjFahEP/0U8iK96DHvvPn7vpMmSVdfHYr/e0OVX8KHHBKCIQdinrHz1z2D+vLL0p9/htqrSy6pinHiieFvcdZZrkwSZjddBcD5vF4QGjKk8GPwz99oo3ADgHJUsKYAxUJTAFQKV3Q744xQHsmvYgdI22wT2mw6aGyKp5+WvKDhJe663akOPFC66qr4DVb57A71+ecuZSf17h1aZaaNe564OkDUwWrHHUPLUwBAijtYFQvBKirBo4+GZV+/emv3f3dA6ZuDzXXWadz3/u9/Q695zxRmencYPFg69lgV1CuvhJzQN9+suc+zx4MGSf37V04rVwAoRwSrBKsoY077dsGMadPqDyY94+lyxePHh8C1oY4/XrroopmD4Lq8y9xF8Qs1y/nCC5L7g3jzVn073QcODJuLkB6eVb7+eumDD6S55pK22CLMzqdxJhxA8aWi3SqAwnCR+UyBqjm4+/pr6fHHG/f93c8+W6Bq330384xnPvn32n//zIGqOZh2egDSwY/Hoou6zXbobuYWtK6W4PxlOmgBaCqCVaDEjB4dX4zes1mvv964759r16lcu1gVquf9NdcU5uejYW65RTrmmJqLC9+i+rOebd1gA+nnn4s9SgCljGAVKDG5bGzy7GRcQJvJMsvE/1vni3brpoLIZSbOwZCXm1FcDky9oS8Tz9B//710441JjgpAuSFYBUqMZ6riOif56z6uMVxyKVsagAPZzTYrXGemVq3ij3GwnMtxKCyngnhDXlxAe+utSY0IQDkiWAVKzHbbhbJImWY/vUTu3fz/K2fcYN4U480x9c3g+me6vqpzFAtlk03iN4Z55thlulD8Gr+5+OGHQo8EQDkjWAVKjLsWuVC/g8a6Aas/n28+yY3hGlvayd/DXZEGDJh19tINAV57zd3nVDAOxPfaK3O6g8fnElb9+hVuDMhN587xx/hxXGyxJEYDoFwRrAIlaLXVpHfflY48sqZrlQO4444L9ze1xacD4gsukL79VnryydB73u07n3suzNoW2mWX1aQx1A7IHfj493UdWXreF5/zm1deOXsetdMA3EgCABqLOqtAGfCruNyK5Dtv1jPI3vX/ySdhxnjXXaV995Xmn7/Yo0NkxAhp441DUFr3bOILjeWXD93W6JoNoDaaAhCsAkBinnhC2m+/MBPvfOOohJU34t12m9SuXbFHCKCU47VG9LcBADTF779L99wTcoN//TWkbXipfKWVVJI23zx0NHMjCpcU8yyq7ytkbjOAysHMKgAkaOzYsGzu4M65np6B9Gyky40dfLB0xRW51dIFgFJGu1UASKGpU6WNNgrtcC3q0hXVzb36aumss4o3PgBII4JVAEjI7beHQDVb04ULL8y95S0AVAKCVQBIiPNU4ziH9dlnkxgNAJQGglUASLDjUy67BKZMSWI0AFAaCFYBICHdu8e3krUuXZIYDQCUBoJVAEjIAQfUbKaqjxs7dOsmrblmkqMCgHQjWAWAhPTpI229df2lqRyo+uZWs+XWjQwAmoJgFQAS4iDUm6xcT3XOOWvus86dpccek/r2LeoQASB1aAoAAEXwww/Sk0+G2qte+t9wQ5oBAKgcU2i3CgDp1r69tOeexR4FAKQf1/EAAABILYJVAAAApBbBKgCUkffflw4/XFplFWn11aWTT5YmTCj2qACg8chZBYAyce650r//HRoPRPVc33pLOv986a67pO23L/YIAaDhmFkFgDLgklgOVK1244G//gqf77KL9O67RRseADQawSoAlDgXIDz77MzNBKIChRdfnOiwACAvCFYBoMRNnCi9915NUFofz67ed1+SowKA/CBYBYAS58YCuZg+vdAjAYD8I1gFgBK3yCJSixbZj3GKwBJLJDUiAMgfglUAKHFt2kj9+oUqANkcemhSIwKA/CFYBYAycMYZ0nzzSbPPPuvXfN+KK0oHHVSMkQFA0xCsAkAZ6NxZeu01af31Z77fs62edX3+ealVq2KNDgAaj6YAAFAmunaVnnlGGjdOevPNEKiuvbbUoUOxRwYAjUewCgBlZsklww0AygFpAAAAAEgtglUAAACkFsEqAAAAUotgFQAAAKlFsAoAAIDUIlgFAABAahGsAgAAILUIVgEAAJBaBKsAAABILYJVAAAApBbBKgAAAFKLYBUAAACpRbAKAACA1CJYBQAAQGoRrAIAACC1CFYBAACQWgSrAAAASC2CVQAAAKQWwSoAAABSi2AVAAAAqTWHykxVVVX1/6dMmVLsoQAAAKAeUZwWxW0VFaz++uuv1f/v3LlzsYcCAACAmLitbdu22Q5Rs6pcQtoS8vfff2vSpElq06aNmjVrplK80nCgPWHCBM0zzzzFHg7yhMe1PPG4li8e2/LE45oeDj8dqHbq1EmzzTZbZc2s+hdeZJFFVOr8IuKFVH54XMsTj2v54rEtTzyu6RA3oxphgxUAAABSi2AVAAAAqUWwmjItWrTQqaeeWv1/lA8e1/LE41q+eGzLE49raSq7DVYAAAAoH8ysAgAAILUIVgEAAJBaBKsAAABILYJVAAAApBbBKgAAAFKLYLXIzj77bPXu3VutWrXSvPPOm9O/cQGHU045RQsttJDmmmsu9enTR59++mnBx4qG+emnn7T77rtXd0nxY7v//vtr6tSpWf/N+uuvX90muPbtkEMOSWzMmNXQoUO1+OKLq2XLlurVq5def/31rMffe++9WmqppaqPX3755fXEE08kNlYU5nG96aabZnld+t8hXV588UVttdVW1e07/Rg99NBDsf/m+eef18orr1xdymrJJZesfqyRPgSrRfbHH39op5120qGHHprzvxk8eLAuvfRSXXXVVRo1apTmnntu9e3bV7///ntBx4qGcaD6wQcfaPjw4Xrssceq30gPOuig2H934IEH6uuvv/7n5scbxXH33XdrwIAB1XUZ33rrLa244orVr7Xvvvuu3uNfffVV9evXr/rC5O2339a2225bfXv//fcTHzvy97iaLzprvy6//PLLRMeMeNOmTat+LH0hkosvvvhCW2yxhTbYYAO988476t+/vw444AA99dRTBR8rGsh1VlF8N954Y1Xbtm1jj/v777+rOnbsWHXBBRf8c98vv/xS1aJFi6o777yzwKNErj788EPXL6564403/rnvySefrGrWrFnVV199lfHfrbfeelVHHXVUQqNEnNVXX73q8MMP/+fzv/76q6pTp05V5557br3H77zzzlVbbLHFTPf16tWr6uCDDy74WFG4xzXX92ekh99/H3zwwazHHHfccVXLLrvsTPftsssuVX379i3w6NBQzKyWGF8JfvPNN9VL/5G2bdtWL2ONHDmyqGNDDT8WXvpfddVV/7nPj9lss81WPRueze2336727dtrueWW06BBg/Tbb78lMGLUt+rx5ptvzvRa8+PnzzO91nx/7ePNM3a8Nkv7cTWn8Cy22GLq3Lmzttlmm+pVE5Q2Xq+lY45iDwAN40DVOnToMNP9/jz6GorPj8WCCy44031zzDGH2rVrl/Vx2m233apPiM65eu+993T88cfr448/1gMPPJDAqFHbDz/8oL/++qve19rYsWPr/Td+bHltlt/j2qNHD91www1aYYUVNHnyZF144YXVew0csC6yyCIJjRz5lun1OmXKFE2fPr16TwjSgZnVAjjhhBNmScave8v0pojKfmyd0+ore2/Mcc7rLbfcogcffFCfffZZXn8PALlbc801tddee6lnz55ab731qi8eF1hgAV199dXFHhpQEZhZLYCBAwdqn332yXpM165dG/W9O3bsWP3/b7/9troaQMSf+40U6Xhs/TjV3azxf//3f9UVAqLHMBdO77Bx48ZpiSWWaOSo0RhOxZh99tmrX1u1+fNMj6Hvb8jxKI3Hta4555xTK620UvXrEqUr0+vVm+mYVU0XgtUC8BW3b4XQpUuX6hfYs88++09w6iUL50E2pKIACvvYeibml19+qc6NW2WVVarve+655/T333//E4DmwjtUrfaFCZLRvHnz6sfOrzXv6Dc/fv78iCOOyPi4++veVRxxNQjfj9J9XOtyGsGYMWO0+eabF3i0KCS/LuuWluP1mlIN3pKFvPryyy+r3n777arTTz+9qnXr1tUf+/brr7/+c0yPHj2qHnjggX8+P++886rmnXfeqocffrjqvffeq9pmm22qunTpUjV9+vQi/Raoz6abblq10korVY0aNarq5ZdfrurWrVtVv379/vn6xIkTqx9bf93GjRtXdcYZZ1SNHj266osvvqh+fLt27Vq17rrrFvG3qGx33XVXdaWNm266qbrCw0EHHVT92vvmm2+qv77nnntWnXDCCf8c/8orr1TNMcccVRdeeGHVRx99VHXqqadWzTnnnFVjxowp4m+Bpj6ufn9+6qmnqj777LOqN998s2rXXXetatmyZdUHH3xQxN8Cdfm8GZ1DHd4MGTKk+mOfZ82PqR/byOeff17VqlWrqmOPPbb69Tp06NCq2WefvWrYsGFF/C1QH4LVItt7772rX1R1byNGjPjnGH/u0im1y1edfPLJVR06dKh+w91oo42qPv744yL9Bsjkxx9/rA5OfREyzzzzVO27774zXYQ4IK39WI8fP746MG3Xrl3147rkkktWv4lOnjy5iL8FLrvssqpFF120qnnz5tUlj1577bWZSo35NVzbPffcU9W9e/fq410W5/HHHy/CqJHPx7V///7/HOv33c0337zqrbfeKtLIkYnfS+s7n0aPpf/vx7buv+nZs2f1Y+vJgdrnWqRHM/+n2LO7AAAAQH2oBgAAAIDUIlgFAABAahGsAgAAILUIVgEAAJBaBKsAAABILYJVAAAApBbBKgAAAFKLYBUAAACpRbAKAACA1CJYBQAAQGoRrAIAAEBp9f+ueV6dN2w+4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "x2, y2 = make_circles(150, factor=0.5, noise=0.1, random_state=30)  # 生成示例数据\n",
    "\n",
    "plt.figure(figsize=(8, 8))  # 绘图\n",
    "plt.scatter(x2[:, 0], x2[:, 1], c=y2, s=40, cmap=\"bwr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64570d51",
   "metadata": {},
   "source": [
    "**------以下是代码注释------**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b06b6f6",
   "metadata": {},
   "source": [
    "让我详细解释 `make_circles()` 方法的使用：\n",
    "\n",
    "# make_circles() 方法详解\n",
    "\n",
    "`make_circles()` 是 scikit-learn 库中的一个数据生成函数，用于生成环形分布的二维数据点。这个方法特别适合用来测试非线性分类算法，因为生成的数据无法通过简单的直线进行线性分割。\n",
    "\n",
    "## 语法格式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "56d957f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "X, y = make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f6c3ce",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 参数说明\n",
    "\n",
    "1. `n_samples=150`\n",
    "   - 要生成的样本点总数\n",
    "   - 在示例中生成150个点\n",
    "   - 这些点会被平均分配到内圈和外圈\n",
    "\n",
    "2. `factor=0.5`\n",
    "   - 内圈与外圈的比例因子(scale factor)\n",
    "   - 取值范围(0, 1)\n",
    "   - 0.5表示内圈的半径是外圈半径的一半\n",
    "   - 值越小，两个圆越分离；值越大，两个圆越接近\n",
    "\n",
    "3. `noise=0.1`\n",
    "   - 高斯噪声的标准差\n",
    "   - 用于给数据添加随机扰动\n",
    "   - 0.1表示适度的噪声水平\n",
    "   - 值越大，数据点的分布越散乱\n",
    "\n",
    "4. `random_state=30`\n",
    "   - 随机数生成器的种子\n",
    "   - 设置固定值可以确保每次生成相同的数据\n",
    "   - 便于实验结果的复现\n",
    "\n",
    "## 返回值\n",
    "\n",
    "返回一个元组 (X, y)：\n",
    "\n",
    "- `x2`：形状为 (150, 2) 的 ndarray\n",
    "  - 包含所有点的坐标\n",
    "  - 每行表示一个点的 (x, y) 坐标\n",
    "\n",
    "- `y2`：形状为 (150,) 的 ndarray\n",
    "  - 类别标签数组\n",
    "  - 0 表示内圈点\n",
    "  - 1 表示外圈点\n",
    "\n",
    "## 用途\n",
    "这种环形分布的数据特别适合用来：\n",
    "1. 测试非线性分类器（如SVM的RBF核）\n",
    "2. 验证核技巧的效果\n",
    "3. 展示线性分类器的局限性\n",
    "\n",
    "因为这些数据点呈现环形分布，无法用简单的直线分割，需要使用更复杂的非线性决策边界才能正确分类。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43eca1b2",
   "metadata": {},
   "source": [
    "**------以上代码注释------**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c8c7c6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "上图明显是一组线性不可分数据，当我们训练支持向量机模型时就需要引入核技巧。例如，我们这里使用下式做一个简单的非线性映射： \n",
    "\n",
    "$$ k\\left ( x_i, x_j \\right )=x_i^2 + x_j^2 \\tag{29} $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "26038f88",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "def kernel_function(xi, xj):\n",
    "    poly = xi**2 + xj**2\n",
    "    return poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0014eaa4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'r')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "from ipywidgets import interact, fixed\n",
    "\n",
    "r = kernel_function(x2[:, 0], x2[:, 1])\n",
    "plt.figure(figsize=(10, 8))\n",
    "ax = plt.subplot(projection=\"3d\")\n",
    "ax.scatter3D(x2[:, 0], x2[:, 1], r, c=y2, s=40, cmap=\"bwr\")\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "ax.set_zlabel(\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c589c4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "上面展示了二维空间点映射到效果维空间的效果。接下来，我们使用 sklearn 中 SVC 方法提供的 RBF 高斯径向基核函数完成实验。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "14ee9e48",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "rbf_svc = SVC(kernel=\"rbf\", gamma=\"auto\")\n",
    "rbf_svc.fit(x2, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d08304d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC(gamma='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f1907030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(x2[:, 0], x2[:, 1], c=y2, s=40, cmap=\"bwr\")\n",
    "\n",
    "svc_plot(rbf_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf590c9",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "同样，我们可以挑战不同的惩罚系数  $C$  ，看一看决策边界和支持向量的变化情况： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2ad3f5ab",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a1ef319c83a487181203fe877174d32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='c', options=(1, 100, 10000), value=1), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.change_c(c)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def change_c(c):\n",
    "    rbf_svc.C = c\n",
    "    rbf_svc.fit(x2, y2)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.scatter(x2[:, 0], x2[:, 1], c=y2, s=40, cmap=\"bwr\")\n",
    "    svc_plot(rbf_svc)\n",
    "\n",
    "interact(change_c, c=[1, 100, 10000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a268d7ce",
   "metadata": {},
   "source": [
    "##  17.15.  多分类支持向量机  # \n",
    "\n",
    "支持向量机最初是为二分类问题设计的，当我们面对多分类问题时，其实同样可以使用支持向量机解决。而解决的方法就是通过组合多个二分类器来实现多分类器的构造。根据构造的方式又分为 2 种方法： \n",
    "\n",
    "  * 一对多法：即训练时依次把某个类别的样本归为一类，剩余的样本归为另一类，这样  $k$  个类别的样本就构造出了  $k$  个支持向量机。 \n",
    "\n",
    "  * 一对一法：即在任意两类样本之间构造一个支持向量机，因此  $k$  个类别的样本就需要设计  $k(k-1) \\div 2$  个支持向量机。 \n",
    "\n",
    "而在 scikit-learn，实现多分类支持向量机通过设定参数 ` decision_function_shape  ` 来确定，其中： \n",
    "\n",
    "  * ` decision_function_shape='ovo'  ` ：代表一对一法。 \n",
    "\n",
    "  * ` decision_function_shape='ovr'  ` ：代表一对多法。 \n",
    "\n",
    "由于这里只需要修改参数，所以就不再赘述了。 \n",
    "\n",
    "最后，我们总结一下 SVM 的优点和缺点。首先，SVM 是一种表现非常不错的方法，尤其是对于非线性分类问题。而且最大的劣势在于计算效率，随着数据集的增大，计算时间陡增。所以，一般我们会在小数据集下应用 SVM，而大数据集基本不予考虑。 \n",
    "\n",
    "##  17.16.  总结  # \n",
    "\n",
    "在本次实验中，我们了解了什么是支持向量机，并探索了函数间隔、几何间隔、拉格朗日对偶性以及核函数特点及使用方法。在机器学习领域众所周知，支持向量机是一个很容易理解，但却很难深入的算法，尽管实验中尽量给出了由简入深的部分数学原理，但本实验还是建议只掌握 scikit-learn 中 SVC 类的使用方法即可。当然，对于数学基础比较好的同学，可以尝试自行推导 SVM 的实现过程。 \n",
    "\n",
    "相关链接 \n",
    "\n",
    "  * [ 维基百科：支持向量机 ](https://zh.wikipedia.org/zh-hans/%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA)\n",
    "\n",
    "  * [ 知乎上关于支持向量机的问题讨论 ](https://www.zhihu.com/question/21094489)\n",
    "\n",
    "  * [ 支持向量机系列文章 ](http://blog.pluskid.org/?page_id=683)\n"
   ]
  }
 ],
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